ggml : remove KQ mask padding

* mtmd: fix patch_size initialized to random value in audio models

* add default hparams

.devops/vulkan.Dockerfile

@@ -1,4 +1,4 @@
-ARG UBUNTU_VERSION=24.04
+ARG UBUNTU_VERSION=25.10
 
 FROM ubuntu:$UBUNTU_VERSION AS build
 
@@ -7,32 +7,16 @@ FROM ubuntu:$UBUNTU_VERSION AS build
 # Install build tools
 RUN apt update && apt install -y git build-essential cmake wget xz-utils
 
-# Install Vulkan SDK
-ARG VULKAN_VERSION=1.4.321.1
-RUN ARCH=$(uname -m) && \
-    wget -qO /tmp/vulkan-sdk.tar.xz https://sdk.lunarg.com/sdk/download/${VULKAN_VERSION}/linux/vulkan-sdk-linux-${ARCH}-${VULKAN_VERSION}.tar.xz && \
-    mkdir -p /opt/vulkan && \
-    tar -xf /tmp/vulkan-sdk.tar.xz -C /tmp --strip-components=1 && \
-    mv /tmp/${ARCH}/* /opt/vulkan/ && \
-    rm -rf /tmp/*
-
 # Install cURL and Vulkan SDK dependencies
 RUN apt install -y libcurl4-openssl-dev curl \
-    libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev
-
-# Set environment variables
-ENV VULKAN_SDK=/opt/vulkan
-ENV PATH=$VULKAN_SDK/bin:$PATH
-ENV LD_LIBRARY_PATH=$VULKAN_SDK/lib:$LD_LIBRARY_PATH
-ENV CMAKE_PREFIX_PATH=$VULKAN_SDK:$CMAKE_PREFIX_PATH
-ENV PKG_CONFIG_PATH=$VULKAN_SDK/lib/pkgconfig:$PKG_CONFIG_PATH
+    libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libvulkan-dev glslc
 
 # Build it
 WORKDIR /app
 
 COPY . .
 
-RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1  -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON && \
+RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=ON -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON && \
     cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib && \
@@ -50,7 +34,7 @@ RUN mkdir -p /app/full \
 FROM ubuntu:$UBUNTU_VERSION AS base
 
 RUN apt-get update \
-    && apt-get install -y libgomp1 curl libvulkan-dev \
+    && apt-get install -y libgomp1 curl libvulkan1 mesa-vulkan-drivers \
     && apt autoremove -y \
     && apt clean -y \
     && rm -rf /tmp/* /var/tmp/* \

.editorconfig

@@ -60,3 +60,11 @@ end_of_line = unset
 charset = unset
 trim_trailing_whitespace = unset
 insert_final_newline = unset
+
+[benches/**]
+indent_style = unset
+indent_size = unset
+end_of_line = unset
+charset = unset
+trim_trailing_whitespace = unset
+insert_final_newline = unset

.github/workflows/build.yml

@@ -161,15 +161,16 @@ jobs:
       - name: Dawn Dependency
         id: dawn-depends
         run: |
-          DAWN_VERSION="v1.0.0"
+          DAWN_VERSION="v2.0.0"
           DAWN_OWNER="reeselevine"
           DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-a1a6b45cced25a3b7f4fb491e0ae70796cc7f22b-macos-latest-Release.tar.gz"
+          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release.zip"
           echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
-          curl -L -o artifact.tar.gz \
+          curl -L -o artifact.zip \
             "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
           mkdir dawn
-          tar -xvf artifact.tar.gz -C dawn --strip-components=1
+          unzip artifact.zip
+          tar -xvf Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release.tar.gz -C dawn --strip-components=1
 
       - name: Build
         id: cmake_build
@@ -521,15 +522,16 @@ jobs:
         id: dawn-depends
         run: |
           sudo apt-get install -y libxrandr-dev libxinerama-dev libxcursor-dev mesa-common-dev libx11-xcb-dev libxi-dev
-          DAWN_VERSION="v1.0.0"
+          DAWN_VERSION="v2.0.0"
           DAWN_OWNER="reeselevine"
           DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-a1a6b45cced25a3b7f4fb491e0ae70796cc7f22b-ubuntu-latest-Release.tar.gz"
+          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-ubuntu-latest-Release.zip"
           echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
-          curl -L -o artifact.tar.gz \
+          curl -L -o artifact.zip \
             "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
           mkdir dawn
-          tar -xvf artifact.tar.gz -C dawn --strip-components=1
+          unzip artifact.zip
+          tar -xvf Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-ubuntu-latest-Release.tar.gz -C dawn --strip-components=1
 
       - name: Build
         id: cmake_build

benches/dgx-spark/aime25_openai__gpt-oss-120b-high_temp1.0_20251109_094547.json

@@ -0,0 +1,6 @@
+{
+  "chars": 2296.1916666666666,
+  "chars:std": 986.051306946325,
+  "score": 0.925,
+  "score:std": 0.26339134382131846
+}

benches/dgx-spark/dgx-spark.md

@@ -0,0 +1,264 @@
+## System info
+
+```bash
+uname --all
+Linux spark-17ed 6.11.0-1016-nvidia #16-Ubuntu SMP PREEMPT_DYNAMIC Sun Sep 21 16:52:46 UTC 2025 aarch64 aarch64 aarch64 GNU/Linux
+
+g++ --version
+g++ (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
+
+nvidia-smi
+Sun Nov  2 10:43:25 2025
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 580.95.05              Driver Version: 580.95.05      CUDA Version: 13.0     |
++-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA GB10                    On  |   0000000F:01:00.0 Off |                  N/A |
+| N/A   35C    P8              4W /  N/A  | Not Supported          |      0%      Default |
+|                                         |                        |                  N/A |
++-----------------------------------------+------------------------+----------------------+
+```
+
+## ggml-org/gpt-oss-20b-GGUF
+
+Model: https://huggingface.co/ggml-org/gpt-oss-20b-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.374 |  1369.01 |    0.383 |    83.64 |    0.757 |   719.01 |
+|   512 |     32 |    2 |   1088 |    0.274 |  3741.35 |    0.659 |    97.14 |    0.933 |  1166.66 |
+|   512 |     32 |    4 |   2176 |    0.526 |  3896.47 |    0.817 |   156.73 |    1.342 |  1621.08 |
+|   512 |     32 |    8 |   4352 |    1.044 |  3925.10 |    0.987 |   259.44 |    2.030 |  2143.56 |
+|   512 |     32 |   16 |   8704 |    2.076 |  3945.84 |    1.248 |   410.32 |    3.324 |  2618.60 |
+|   512 |     32 |   32 |  17408 |    4.170 |  3929.28 |    1.630 |   628.40 |    5.799 |  3001.76 |
+|  4096 |     32 |    1 |   4128 |    1.083 |  3782.66 |    0.394 |    81.21 |    1.477 |  2795.13 |
+|  4096 |     32 |    2 |   8256 |    2.166 |  3782.72 |    0.725 |    88.28 |    2.891 |  2856.14 |
+|  4096 |     32 |    4 |  16512 |    4.333 |  3780.88 |    0.896 |   142.82 |    5.230 |  3157.38 |
+|  4096 |     32 |    8 |  33024 |    8.618 |  3802.14 |    1.155 |   221.69 |    9.773 |  3379.08 |
+|  4096 |     32 |   16 |  66048 |   17.330 |  3781.73 |    1.598 |   320.34 |   18.928 |  3489.45 |
+|  4096 |     32 |   32 | 132096 |   34.671 |  3780.48 |    2.336 |   438.35 |   37.007 |  3569.51 |
+|  8192 |     32 |    1 |   8224 |    2.233 |  3668.56 |    0.438 |    72.98 |    2.671 |  3078.44 |
+|  8192 |     32 |    2 |  16448 |    4.425 |  3702.95 |    0.756 |    84.66 |    5.181 |  3174.95 |
+|  8192 |     32 |    4 |  32896 |    8.859 |  3698.64 |    0.967 |   132.38 |    9.826 |  3347.72 |
+|  8192 |     32 |    8 |  65792 |   17.714 |  3699.57 |    1.277 |   200.52 |   18.991 |  3464.35 |
+|  8192 |     32 |   16 | 131584 |   35.494 |  3692.84 |    1.841 |   278.12 |   37.335 |  3524.46 |
+|  8192 |     32 |   32 | 263168 |   70.949 |  3694.82 |    2.798 |   365.99 |   73.747 |  3568.53 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |      3714.25 ± 20.36 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         86.58 ± 0.43 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |      3445.17 ± 17.85 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         81.72 ± 0.53 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |      3218.78 ± 11.34 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         74.86 ± 0.64 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |       2732.83 ± 7.17 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         71.57 ± 0.51 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |      2119.75 ± 12.81 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         62.33 ± 0.24 |
+
+build: eeee367de (6989)
+
+## ggml-org/gpt-oss-120b-GGUF
+
+Model: https://huggingface.co/ggml-org/gpt-oss-120b-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.571 |   897.18 |    0.543 |    58.96 |    1.113 |   488.60 |
+|   512 |     32 |    2 |   1088 |    0.593 |  1725.37 |    1.041 |    61.45 |    1.635 |   665.48 |
+|   512 |     32 |    4 |   2176 |    1.043 |  1963.15 |    1.334 |    95.95 |    2.377 |   915.36 |
+|   512 |     32 |    8 |   4352 |    2.099 |  1951.63 |    1.717 |   149.07 |    3.816 |  1140.45 |
+|   512 |     32 |   16 |   8704 |    4.207 |  1947.12 |    2.311 |   221.56 |    6.518 |  1335.35 |
+|   512 |     32 |   32 |  17408 |    8.422 |  1945.36 |    3.298 |   310.46 |   11.720 |  1485.27 |
+|  4096 |     32 |    1 |   4128 |    2.138 |  1915.88 |    0.571 |    56.09 |    2.708 |  1524.12 |
+|  4096 |     32 |    2 |   8256 |    4.266 |  1920.25 |    1.137 |    56.27 |    5.404 |  1527.90 |
+|  4096 |     32 |    4 |  16512 |    8.564 |  1913.02 |    1.471 |    86.99 |   10.036 |  1645.29 |
+|  4096 |     32 |    8 |  33024 |   17.092 |  1917.19 |    1.979 |   129.33 |   19.071 |  1731.63 |
+|  4096 |     32 |   16 |  66048 |   34.211 |  1915.65 |    2.850 |   179.66 |   37.061 |  1782.15 |
+|  4096 |     32 |   32 | 132096 |   68.394 |  1916.44 |    4.381 |   233.72 |   72.775 |  1815.13 |
+|  8192 |     32 |    1 |   8224 |    4.349 |  1883.45 |    0.620 |    51.65 |    4.969 |  1655.04 |
+|  8192 |     32 |    2 |  16448 |    8.674 |  1888.83 |    1.178 |    54.33 |    9.852 |  1669.48 |
+|  8192 |     32 |    4 |  32896 |   17.351 |  1888.55 |    1.580 |    81.01 |   18.931 |  1737.68 |
+|  8192 |     32 |    8 |  65792 |   34.743 |  1886.31 |    2.173 |   117.80 |   36.916 |  1782.20 |
+|  8192 |     32 |   16 | 131584 |   69.413 |  1888.29 |    3.297 |   155.28 |   72.710 |  1809.70 |
+|  8192 |     32 |   32 | 263168 |  138.903 |  1887.24 |    5.004 |   204.63 |  143.907 |  1828.73 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |       1919.36 ± 5.01 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         60.40 ± 0.30 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |       1825.30 ± 6.37 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         56.94 ± 0.29 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |       1739.19 ± 6.00 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         52.51 ± 0.42 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |       1536.75 ± 4.27 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         49.33 ± 0.27 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |       1255.85 ± 3.26 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         42.99 ± 0.18 |
+
+build: eeee367de (6989)
+
+## ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
+
+Model: https://huggingface.co/ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.398 |  1285.90 |    0.530 |    60.41 |    0.928 |   586.27 |
+|   512 |     32 |    2 |   1088 |    0.386 |  2651.65 |    0.948 |    67.50 |    1.334 |   815.38 |
+|   512 |     32 |    4 |   2176 |    0.666 |  3076.37 |    1.209 |   105.87 |    1.875 |  1160.71 |
+|   512 |     32 |    8 |   4352 |    1.325 |  3091.39 |    1.610 |   158.98 |    2.935 |  1482.65 |
+|   512 |     32 |   16 |   8704 |    2.664 |  3075.58 |    2.150 |   238.19 |    4.813 |  1808.39 |
+|   512 |     32 |   32 |  17408 |    5.336 |  3070.31 |    2.904 |   352.59 |    8.240 |  2112.50 |
+|  4096 |     32 |    1 |   4128 |    1.444 |  2836.81 |    0.581 |    55.09 |    2.025 |  2038.81 |
+|  4096 |     32 |    2 |   8256 |    2.872 |  2852.14 |    1.084 |    59.06 |    3.956 |  2086.99 |
+|  4096 |     32 |    4 |  16512 |    5.744 |  2852.32 |    1.440 |    88.90 |    7.184 |  2298.47 |
+|  4096 |     32 |    8 |  33024 |   11.463 |  2858.68 |    2.068 |   123.78 |   13.531 |  2440.65 |
+|  4096 |     32 |   16 |  66048 |   22.915 |  2859.95 |    3.018 |   169.67 |   25.933 |  2546.90 |
+|  4096 |     32 |   32 | 132096 |   45.956 |  2852.10 |    4.609 |   222.18 |   50.565 |  2612.39 |
+|  8192 |     32 |    1 |   8224 |    3.063 |  2674.72 |    0.693 |    46.20 |    3.755 |  2189.92 |
+|  8192 |     32 |    2 |  16448 |    6.109 |  2681.87 |    1.214 |    52.71 |    7.323 |  2245.98 |
+|  8192 |     32 |    4 |  32896 |   12.197 |  2686.63 |    1.682 |    76.11 |   13.878 |  2370.30 |
+|  8192 |     32 |    8 |  65792 |   24.409 |  2684.94 |    2.556 |   100.17 |   26.965 |  2439.95 |
+|  8192 |     32 |   16 | 131584 |   48.753 |  2688.50 |    3.994 |   128.20 |   52.747 |  2494.64 |
+|  8192 |     32 |   32 | 263168 |   97.508 |  2688.42 |    6.528 |   156.86 |  104.037 |  2529.57 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |       2925.55 ± 4.25 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         62.80 ± 0.27 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |       2531.01 ± 6.79 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         55.86 ± 0.33 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |       2244.39 ± 5.33 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         45.95 ± 0.33 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |       1783.17 ± 3.68 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         39.07 ± 0.10 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |       1241.90 ± 3.13 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         29.92 ± 0.06 |
+
+build: eeee367de (6989)
+
+## ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
+
+Model: https://huggingface.co/ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.211 |  2421.57 |    1.055 |    30.33 |    1.266 |   429.57 |
+|   512 |     32 |    2 |   1088 |    0.419 |  2441.34 |    1.130 |    56.65 |    1.549 |   702.32 |
+|   512 |     32 |    4 |   2176 |    0.873 |  2345.54 |    1.174 |   108.99 |    2.048 |  1062.74 |
+|   512 |     32 |    8 |   4352 |    1.727 |  2371.85 |    1.254 |   204.22 |    2.980 |  1460.19 |
+|   512 |     32 |   16 |   8704 |    3.452 |  2373.22 |    1.492 |   343.16 |    4.944 |  1760.56 |
+|   512 |     32 |   32 |  17408 |    6.916 |  2368.93 |    1.675 |   611.51 |    8.591 |  2026.36 |
+|  4096 |     32 |    1 |   4128 |    1.799 |  2277.26 |    1.084 |    29.51 |    2.883 |  1431.91 |
+|  4096 |     32 |    2 |   8256 |    3.577 |  2290.01 |    1.196 |    53.50 |    4.774 |  1729.51 |
+|  4096 |     32 |    4 |  16512 |    7.172 |  2284.36 |    1.313 |    97.50 |    8.485 |  1946.00 |
+|  4096 |     32 |    8 |  33024 |   14.341 |  2284.96 |    1.520 |   168.46 |   15.860 |  2082.18 |
+|  4096 |     32 |   16 |  66048 |   28.675 |  2285.44 |    1.983 |   258.21 |   30.658 |  2154.33 |
+|  4096 |     32 |   32 | 132096 |   57.354 |  2285.32 |    2.640 |   387.87 |   59.994 |  2201.82 |
+|  8192 |     32 |    1 |   8224 |    3.701 |  2213.75 |    1.119 |    28.59 |    4.820 |  1706.34 |
+|  8192 |     32 |    2 |  16448 |    7.410 |  2211.19 |    1.272 |    50.31 |    8.682 |  1894.56 |
+|  8192 |     32 |    4 |  32896 |   14.802 |  2213.83 |    1.460 |    87.68 |   16.261 |  2022.96 |
+|  8192 |     32 |    8 |  65792 |   29.609 |  2213.35 |    1.781 |   143.74 |   31.390 |  2095.93 |
+|  8192 |     32 |   16 | 131584 |   59.229 |  2212.96 |    2.495 |   205.17 |   61.725 |  2131.79 |
+|  8192 |     32 |   32 | 263168 |  118.449 |  2213.15 |    3.714 |   275.75 |  122.162 |  2154.25 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |       2272.74 ± 4.68 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         30.66 ± 0.02 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |       2107.80 ± 9.55 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         29.71 ± 0.05 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |       1937.80 ± 6.75 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         28.86 ± 0.04 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |       1641.12 ± 1.78 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         27.24 ± 0.04 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |       1296.02 ± 2.67 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         23.78 ± 0.03 |
+
+build: eeee367de (6989)
+
+## ggml-org/gemma-3-4b-it-qat-GGUF
+
+Model: https://huggingface.co/ggml-org/gemma-3-4b-it-qat-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.094 |  5434.73 |    0.394 |    81.21 |    0.488 |  1114.15 |
+|   512 |     32 |    2 |   1088 |    0.168 |  6091.68 |    0.498 |   128.52 |    0.666 |  1633.41 |
+|   512 |     32 |    4 |   2176 |    0.341 |  6010.68 |    0.542 |   236.37 |    0.882 |  2466.43 |
+|   512 |     32 |    8 |   4352 |    0.665 |  6161.46 |    0.678 |   377.74 |    1.342 |  3241.72 |
+|   512 |     32 |   16 |   8704 |    1.323 |  6193.19 |    0.902 |   567.41 |    2.225 |  3911.74 |
+|   512 |     32 |   32 |  17408 |    2.642 |  6202.03 |    1.231 |   832.03 |    3.872 |  4495.36 |
+|  4096 |     32 |    1 |   4128 |    0.701 |  5840.49 |    0.439 |    72.95 |    1.140 |  3621.23 |
+|  4096 |     32 |    2 |   8256 |    1.387 |  5906.82 |    0.574 |   111.48 |    1.961 |  4210.12 |
+|  4096 |     32 |    4 |  16512 |    2.758 |  5940.33 |    0.651 |   196.58 |    3.409 |  4843.33 |
+|  4096 |     32 |    8 |  33024 |    5.491 |  5967.56 |    0.876 |   292.40 |    6.367 |  5187.12 |
+|  4096 |     32 |   16 |  66048 |   10.978 |  5969.58 |    1.275 |   401.69 |   12.253 |  5390.38 |
+|  4096 |     32 |   32 | 132096 |   21.944 |  5972.93 |    1.992 |   514.16 |   23.936 |  5518.73 |
+|  8192 |     32 |    1 |   8224 |    1.402 |  5841.91 |    0.452 |    70.73 |    1.855 |  4434.12 |
+|  8192 |     32 |    2 |  16448 |    2.793 |  5865.34 |    0.637 |   100.55 |    3.430 |  4795.51 |
+|  8192 |     32 |    4 |  32896 |    5.564 |  5889.64 |    0.770 |   166.26 |    6.334 |  5193.95 |
+|  8192 |     32 |    8 |  65792 |   11.114 |  5896.44 |    1.122 |   228.07 |   12.237 |  5376.51 |
+|  8192 |     32 |   16 | 131584 |   22.210 |  5901.38 |    1.789 |   286.15 |   24.000 |  5482.74 |
+|  8192 |     32 |   32 | 263168 |   44.382 |  5906.56 |    3.044 |   336.38 |   47.426 |  5549.02 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |      5810.04 ± 21.71 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         84.54 ± 0.18 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |       5288.04 ± 3.54 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         78.82 ± 1.37 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |      4960.43 ± 16.64 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         74.13 ± 0.30 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |      4495.92 ± 31.11 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         72.37 ± 0.29 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |      3746.90 ± 40.01 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         63.02 ± 0.20 |
+
+build: eeee367de (6989)
+

common/CMakeLists.txt

@@ -56,6 +56,8 @@ add_library(${TARGET} STATIC
     common.h
     console.cpp
     console.h
+    download.cpp
+    download.h
     http.h
     json-partial.cpp
     json-partial.h

common/arg.h

@@ -59,8 +59,8 @@ struct common_arg {
     common_arg & set_sparam();
     bool in_example(enum llama_example ex);
     bool is_exclude(enum llama_example ex);
-    bool get_value_from_env(std::string & output);
-    bool has_value_from_env();
+    bool get_value_from_env(std::string & output) const;
+    bool has_value_from_env() const;
     std::string to_string();
 };
 

common/common.cpp

@@ -908,6 +908,39 @@ std::string fs_get_cache_file(const std::string & filename) {
     return cache_directory + filename;
 }
 
+std::vector<common_file_info> fs_list_files(const std::string & path) {
+    std::vector<common_file_info> files;
+    if (path.empty()) return files;
+
+    std::filesystem::path dir(path);
+    if (!std::filesystem::exists(dir) || !std::filesystem::is_directory(dir)) {
+        return files;
+    }
+
+    for (const auto & entry : std::filesystem::directory_iterator(dir)) {
+        try {
+            // Only include regular files (skip directories)
+            const auto & p = entry.path();
+            if (std::filesystem::is_regular_file(p)) {
+                common_file_info info;
+                info.path = p.string();
+                info.name = p.filename().string();
+                try {
+                    info.size = static_cast<size_t>(std::filesystem::file_size(p));
+                } catch (const std::filesystem::filesystem_error &) {
+                    info.size = 0;
+                }
+                files.push_back(std::move(info));
+            }
+        } catch (const std::filesystem::filesystem_error &) {
+            // skip entries we cannot inspect
+            continue;
+        }
+    }
+
+    return files;
+}
+
 
 //
 // Model utils

common/common.h

@@ -460,7 +460,8 @@ struct common_params {
     float slot_prompt_similarity = 0.1f;
 
     // batched-bench params
-    bool is_pp_shared = false;
+    bool is_pp_shared   = false;
+    bool is_tg_separate = false;
 
     std::vector<int32_t> n_pp;
     std::vector<int32_t> n_tg;
@@ -611,6 +612,13 @@ bool fs_create_directory_with_parents(const std::string & path);
 std::string fs_get_cache_directory();
 std::string fs_get_cache_file(const std::string & filename);
 
+struct common_file_info {
+    std::string path;
+    std::string name;
+    size_t      size = 0; // in bytes
+};
+std::vector<common_file_info> fs_list_files(const std::string & path);
+
 //
 // Model utils
 //

common/download.h

@@ -0,0 +1,55 @@
+#pragma once
+
+#include <string>
+
+struct common_params_model;
+
+//
+// download functionalities
+//
+
+struct common_cached_model_info {
+    std::string manifest_path;
+    std::string user;
+    std::string model;
+    std::string tag;
+    size_t      size = 0; // GGUF size in bytes
+    std::string to_string() const {
+        return user + "/" + model + ":" + tag;
+    }
+};
+
+struct common_hf_file_res {
+    std::string repo; // repo name with ":tag" removed
+    std::string ggufFile;
+    std::string mmprojFile;
+};
+
+/**
+ * Allow getting the HF file from the HF repo with tag (like ollama), for example:
+ * - bartowski/Llama-3.2-3B-Instruct-GGUF:q4
+ * - bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
+ * - bartowski/Llama-3.2-3B-Instruct-GGUF:q5_k_s
+ * Tag is optional, default to "latest" (meaning it checks for Q4_K_M first, then Q4, then if not found, return the first GGUF file in repo)
+ *
+ * Return pair of <repo, file> (with "repo" already having tag removed)
+ *
+ * Note: we use the Ollama-compatible HF API, but not using the blobId. Instead, we use the special "ggufFile" field which returns the value for "hf_file". This is done to be backward-compatible with existing cache files.
+ */
+common_hf_file_res common_get_hf_file(
+    const std::string & hf_repo_with_tag,
+    const std::string & bearer_token,
+    bool offline);
+
+// returns true if download succeeded
+bool common_download_model(
+    const common_params_model & model,
+    const std::string & bearer_token,
+    bool offline);
+
+// returns list of cached models
+std::vector<common_cached_model_info> common_list_cached_models();
+
+// resolve and download model from Docker registry
+// return local path to downloaded model file
+std::string common_docker_resolve_model(const std::string & docker);

convert_hf_to_gguf.py

@@ -218,8 +218,7 @@ class ModelBase:
             logger.info(f"gguf: indexing model part '{part_name}'")
             ctx: ContextManager[Any]
             if is_safetensors:
-                from safetensors import safe_open
-                ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu"))
+                ctx = cast(ContextManager[Any], gguf.utility.SafetensorsLocal(self.dir_model / part_name))
             else:
                 ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
 
@@ -228,18 +227,18 @@ class ModelBase:
 
                 for name in model_part.keys():
                     if is_safetensors:
+                        data: gguf.utility.LocalTensor = model_part[name]
                         if self.lazy:
-                            data = model_part.get_slice(name)
-                            data_gen = lambda data=data: LazyTorchTensor.from_safetensors_slice(data)  # noqa: E731
+                            data_gen = lambda data=data: LazyTorchTensor.from_local_tensor(data)  # noqa: E731
                         else:
-                            data = model_part.get_tensor(name)
-                            data_gen = lambda data=data: data  # noqa: E731
+                            dtype = LazyTorchTensor._dtype_str_map[data.dtype]
+                            data_gen = lambda data=data, dtype=dtype: torch.from_numpy(data.mmap_bytes()).view(dtype).reshape(data.shape)  # noqa: E731
                     else:
-                        data = model_part[name]
+                        data_torch: Tensor = model_part[name]
                         if self.lazy:
-                            data_gen = lambda data=data: LazyTorchTensor.from_eager(data)  # noqa: E731
+                            data_gen = lambda data=data_torch: LazyTorchTensor.from_eager(data)  # noqa: E731
                         else:
-                            data_gen = lambda data=data: data  # noqa: E731
+                            data_gen = lambda data=data_torch: data  # noqa: E731
                     tensors[name] = data_gen
 
         # verify tensor name presence and identify potentially missing files
@@ -278,15 +277,14 @@ class ModelBase:
                 # The scale is inverted
                 return data / scale.float()
 
-            def dequant_simple(weight: Tensor, scale: Tensor) -> Tensor:
+            def dequant_simple(weight: Tensor, scale: Tensor, block_size: Sequence[int] | None = None) -> Tensor:
                 scale = scale.float()
 
-                if (weight_block_size := quant_config.get("weight_block_size")):
-                    # TODO: make sure it's a list of integers
-                    for i, size in enumerate(weight_block_size):
+                if block_size is not None:
+                    for i, size in enumerate(block_size):
                         scale = scale.repeat_interleave(size, i)
-                # unpad the scale (e.g. when the tensor size isn't a multiple of the block size)
-                scale = scale[tuple(slice(0, size) for size in weight.shape)]
+                    # unpad the scale (e.g. when the tensor size isn't a multiple of the block size)
+                    scale = scale[tuple(slice(0, size) for size in weight.shape)]
 
                 return weight.float() * scale
 
@@ -333,6 +331,40 @@ class ModelBase:
 
                 return (scales[g_idx].float() * (weight - zeros[g_idx]).float()).T
 
+            def dequant_packed(w: Tensor, scale: Tensor, shape_tensor: Tensor, zero_point: Tensor | None, num_bits: int, group_size: int):
+                assert w.dtype == torch.int32
+                shape = tuple(shape_tensor.tolist())
+                assert len(shape) == 2
+                mask = (1 << num_bits) - 1
+
+                shifts = torch.arange(0, 32 - (num_bits - 1), num_bits, dtype=torch.int32)
+                if self.lazy:
+                    shifts = LazyTorchTensor.from_eager(shifts)
+
+                if zero_point is None:
+                    offset = 1 << (num_bits - 1)
+                else:
+                    assert len(zero_point.shape) == 2
+                    offset = (zero_point.unsqueeze(1) >> shifts.reshape(1, -1, 1)) & mask
+                    offset = offset.reshape(-1, zero_point.shape[1])
+                    # trim padding, and prepare for broadcast
+                    # NOTE: the zero-point is packed along dim 0
+                    offset = offset[:shape[0], :].unsqueeze(-1)
+
+                # extract values
+                # NOTE: the weights are packed along dim 1
+                unpacked = (w.unsqueeze(-1) >> shifts.reshape(1, 1, -1)) & mask
+                unpacked = unpacked.reshape(shape[0], -1)
+
+                # trim padding
+                unpacked = unpacked[:, :shape[1]]
+
+                # prepare for broadcast of the scale
+                unpacked = unpacked.reshape(shape[0], (unpacked.shape[-1] + group_size - 1) // group_size, group_size)
+                unpacked = unpacked - offset
+
+                return (unpacked * scale.unsqueeze(-1).float()).reshape(shape)
+
             if quant_method == "bitnet":
                 for name in self.model_tensors.keys():
                     if name.endswith(".weight_scale"):
@@ -342,12 +374,13 @@ class ModelBase:
                         self.model_tensors[weight_name] = lambda w=w, s=s: dequant_bitnet(w(), s())
                         tensors_to_remove.append(name)
             elif quant_method == "fp8":
+                block_size = quant_config.get("weight_block_size")
                 for name in self.model_tensors.keys():
                     if name.endswith(".weight_scale_inv"):
                         weight_name = name.removesuffix("_scale_inv")
                         w = self.model_tensors[weight_name]
                         s = self.model_tensors[name]
-                        self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s())
+                        self.model_tensors[weight_name] = lambda w=w, s=s, bs=block_size: dequant_simple(w(), s(), bs)
                         tensors_to_remove.append(name)
             elif quant_method == "gptq":
                 for name in self.model_tensors.keys():
@@ -371,6 +404,49 @@ class ModelBase:
                                 ".scales",
                             )
                         ]
+            elif quant_method == "compressed-tensors":
+                quant_format = quant_config["format"]
+                groups = quant_config["config_groups"]
+                if len(groups) > 1:
+                    raise NotImplementedError("Can't handle multiple config groups for compressed-tensors yet")
+                weight_config = tuple(groups.values())[0]["weights"]
+
+                if quant_format == "float-quantized" or quant_format == "int-quantized" or quant_format == "naive-quantized":
+                    block_size = weight_config.get("block_structure", None)
+                    strategy = weight_config.get("strategy")
+                    assert strategy == "channel" or strategy == "block"
+                    assert weight_config.get("group_size") is None  # didn't find a model using this yet
+                    for name in self.model_tensors.keys():
+                        if name.endswith(".weight_scale"):
+                            weight_name = name.removesuffix("_scale")
+                            w = self.model_tensors[weight_name]
+                            s = self.model_tensors[name]
+                            self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s(), block_size)
+                            tensors_to_remove.append(name)
+                elif quant_format == "pack-quantized":
+                    assert weight_config.get("strategy") == "group"
+                    assert weight_config.get("type", "int") == "int"
+                    num_bits = weight_config.get("num_bits")
+                    group_size = weight_config.get("group_size")
+                    assert isinstance(num_bits, int)
+                    assert isinstance(group_size, int)
+                    for name in self.model_tensors.keys():
+                        if name.endswith(".weight_packed"):
+                            base_name = name.removesuffix("_packed")
+                            w = self.model_tensors[name]
+                            scale = self.model_tensors[base_name + "_scale"]
+                            shape = self.model_tensors[base_name + "_shape"]
+                            zero_point = self.model_tensors.get(base_name + "_zero_point", lambda: None)
+                            new_tensors[base_name] = (
+                                lambda w=w, scale=scale, shape=shape, zero_point=zero_point: dequant_packed(
+                                    w(), scale(), shape(), zero_point(), num_bits, group_size,
+                                )
+                            )
+                            tensors_to_remove += [base_name + n for n in ("_packed", "_shape", "_scale")]
+                            if (base_name + "_zero_point") in self.model_tensors:
+                                tensors_to_remove.append(base_name + "_zero_point")
+                else:
+                    raise NotImplementedError(f"Quant format {quant_format!r} for method {quant_method!r} is not yet supported")
             else:
                 raise NotImplementedError(f"Quant method is not yet supported: {quant_method!r}")
 
@@ -10002,6 +10078,16 @@ class LazyTorchTensor(gguf.LazyBase):
         lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(st_slice,), func=lambda s: s[...] if len(s.get_shape()) == 0 else s[:])
         return cast(torch.Tensor, lazy)
 
+    @classmethod
+    def from_local_tensor(cls, t: gguf.utility.LocalTensor) -> Tensor:
+        def load_tensor(tensor: gguf.utility.LocalTensor) -> Tensor:
+            dtype = cls._dtype_str_map[tensor.dtype]
+            return torch.from_numpy(tensor.mmap_bytes()).view(dtype).reshape(tensor.shape)
+        dtype = cls._dtype_str_map[t.dtype]
+        shape = t.shape
+        lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(t,), func=lambda r: load_tensor(r))
+        return cast(torch.Tensor, lazy)
+
     @classmethod
     def from_remote_tensor(cls, remote_tensor: gguf.utility.RemoteTensor):
         dtype = cls._dtype_str_map[remote_tensor.dtype]

ggml/CMakeLists.txt

@@ -168,7 +168,7 @@ option(GGML_RV_ZFH           "ggml: enable riscv zfh"        ON)
 option(GGML_RV_ZVFH          "ggml: enable riscv zvfh"       ON)
 option(GGML_RV_ZICBOP        "ggml: enable riscv zicbop"     ON)
 option(GGML_XTHEADVECTOR     "ggml: enable xtheadvector"     OFF)
-option(GGML_VXE              "ggml: enable vxe"              ON)
+option(GGML_VXE              "ggml: enable vxe"              ${GGML_NATIVE})
 
 option(GGML_CPU_ALL_VARIANTS "ggml: build all variants of the CPU backend (requires GGML_BACKEND_DL)" OFF)
 set(GGML_CPU_ARM_ARCH        "" CACHE STRING "ggml: CPU architecture for ARM")

ggml/include/ggml.h

@@ -2220,7 +2220,7 @@ extern "C" {
             struct ggml_tensor  * a,
             int                   k);
 
-#define GGML_KQ_MASK_PAD 64
+#define GGML_KQ_MASK_PAD 1
 
     // q:    [n_embd_k, n_batch,     n_head,    ne3 ]
     // k:    [n_embd_k, n_kv,        n_head_kv, ne3 ]

ggml/src/ggml-cpu/arch/riscv/quants.c

@@ -580,16 +580,19 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
             const float dmin = -y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
             uint8_t *patmp = atmp;
             int vsums;
-            int tmp;
+            int tmp, t1, t2, t3, t4, t5, t6, t7;
             __asm__ __volatile__(
                 "vsetivli zero, 16, e8, m1\n\t"
                 "vmv.v.x v8, zero\n\t"
+                "lb zero, 15(%[sc])\n\t"
                 "vle8.v v1, (%[sc])\n\t"
+                "vle8.v v2, (%[bsums])\n\t"
+                "addi %[tmp], %[bsums], 16\n\t"
                 "vand.vi v0, v1, 0xF\n\t"
                 "vsrl.vi v1, v1, 4\n\t"
+                "vle8.v v3, (%[tmp])\n\t"
                 "vse8.v v0, (%[scale])\n\t"
                 "vsetivli zero, 16, e16, m2\n\t"
-                "vle16.v v2, (%[bsums])\n\t"
                 "vzext.vf2 v0, v1\n\t"
                 "vwmul.vv v4, v0, v2\n\t"
                 "vsetivli zero, 16, e32, m4\n\t"
@@ -608,46 +611,89 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
             for (int j = 0; j < QK_K/128; ++j) {
                 __asm__ __volatile__(
-                    "vsetvli zero, %[vl32], e8, m2\n\t"
+                    "lb zero, 31(%[q2])\n\t"
+                    "addi %[tmp], %[q2], 16\n\t"
+                    "addi %[t1], %[q8], 16\n\t"
+                    "vsetivli zero, 16, e8, m1\n\t"
                     "vle8.v v0, (%[q2])\n\t"
+                    "vle8.v v1, (%[tmp])\n\t"
                     "vsrl.vi v2, v0, 2\n\t"
+                    "vsrl.vi v3, v1, 2\n\t"
                     "vsrl.vi v4, v0, 4\n\t"
-                    "vsrl.vi v6, v0, 6\n\t"
-                    "vand.vi v0, v0, 0x3\n\t"
-                    "vand.vi v2, v2, 0x3\n\t"
-                    "vand.vi v4, v4, 0x3\n\t"
-                    "vsetvli zero, %[vl128], e8, m8\n\t"
+                    "addi %[tmp], %[q8], 32\n\t"
                     "vle8.v v8, (%[q8])\n\t"
-                    "vsetvli zero, %[vl64], e8, m4\n\t"
+                    "vle8.v v9, (%[t1])\n\t"
+                    "addi %[t1], %[t1], 32\n\t"
+                    "vsrl.vi v5, v1, 4\n\t"
+                    "vsrl.vi v6, v0, 6\n\t"
+                    "vsrl.vi v7, v1, 6\n\t"
+                    "vle8.v v10, (%[tmp])\n\t"
+                    "vle8.v v11, (%[t1])\n\t"
+                    "addi %[tmp], %[tmp], 32\n\t"
+                    "addi %[t1], %[t1], 32\n\t"
+                    "vand.vi v0, v0, 0x3\n\t"
+                    "vand.vi v1, v1, 0x3\n\t"
+                    "vand.vi v2, v2, 0x3\n\t"
+                    "vle8.v v12, (%[tmp])\n\t"
+                    "vle8.v v13, (%[t1])\n\t"
+                    "addi %[tmp], %[tmp], 32\n\t"
+                    "addi %[t1], %[t1], 32\n\t"
+                    "vand.vi v3, v3, 0x3\n\t"
+                    "vand.vi v4, v4, 0x3\n\t"
+                    "vand.vi v5, v5, 0x3\n\t"
+                    "vle8.v v14, (%[tmp])\n\t"
+                    "vle8.v v15, (%[t1])\n\t"
                     "vwmul.vv v16, v0, v8\n\t"
+                    "vwmul.vv v18, v1, v9\n\t"
+                    "vwmul.vv v20, v2, v10\n\t"
+                    "vwmul.vv v22, v3, v11\n\t"
                     "vwmul.vv v24, v4, v12\n\t"
-                    "vsetivli zero, 16, e16, m2\n\t"
+                    "vwmul.vv v26, v5, v13\n\t"
+                    "vwmul.vv v28, v6, v14\n\t"
+                    "vwmul.vv v30, v7, v15\n\t"
+                    "vsetivli zero, 8, e16, m1\n\t"
                     "vmv.v.x v0, zero\n\t"
-                    "vwredsum.vs v10, v16, v0\n\t"
+                    "lbu %[tmp], 0(%[scale])\n\t"
+                    "vwredsum.vs v8, v16, v0\n\t"
                     "vwredsum.vs v9, v18, v0\n\t"
-                    "vwredsum.vs v8, v20, v0\n\t"
-                    "vwredsum.vs v7, v22, v0\n\t"
-                    "vwredsum.vs v11, v24, v0\n\t"
-                    "vwredsum.vs v12, v26, v0\n\t"
-                    "vwredsum.vs v13, v28, v0\n\t"
-                    "vwredsum.vs v14, v30, v0\n\t"
+                    "lbu %[t1], 1(%[scale])\n\t"
+                    "vwredsum.vs v10, v20, v0\n\t"
+                    "vwredsum.vs v11, v22, v0\n\t"
+                    "lbu %[t2], 2(%[scale])\n\t"
+                    "vwredsum.vs v12, v24, v0\n\t"
+                    "vwredsum.vs v13, v26, v0\n\t"
+                    "lbu %[t3], 3(%[scale])\n\t"
+                    "vwredsum.vs v14, v28, v0\n\t"
+                    "vwredsum.vs v15, v30, v0\n\t"
+                    "lbu %[t4], 4(%[scale])\n\t"
+                    "vwredsum.vs v8, v17, v8\n\t"
+                    "vwredsum.vs v9, v19, v9\n\t"
+                    "lbu %[t5], 5(%[scale])\n\t"
+                    "vwredsum.vs v10, v21, v10\n\t"
+                    "vwredsum.vs v11, v23, v11\n\t"
+                    "lbu %[t6], 6(%[scale])\n\t"
+                    "vwredsum.vs v12, v25, v12\n\t"
+                    "vwredsum.vs v13, v27, v13\n\t"
+                    "lbu %[t7], 7(%[scale])\n\t"
+                    "vwredsum.vs v14, v29, v14\n\t"
+                    "vwredsum.vs v15, v31, v15\n\t"
                     "vsetivli zero, 4, e32, m1\n\t"
-                    "vslideup.vi v10, v9, 1\n\t"
-                    "vslideup.vi v8, v7, 1\n\t"
-                    "vslideup.vi v11, v12, 1\n\t"
-                    "vslideup.vi v13, v14, 1\n\t"
-                    "vslideup.vi v10, v8, 2\n\t"
-                    "vslideup.vi v11, v13, 2\n\t"
-                    "vsetivli zero, 8, e32, m2\n\t"
-                    "vle8.v v15, (%[scale])\n\t"
-                    "vzext.vf4 v12, v15\n\t"
-                    "vmul.vv v10, v10, v12\n\t"
-                    "vredsum.vs v0, v10, v0\n\t"
+                    "vmul.vx v0, v8, %[tmp]\n\t"
+                    "vmul.vx v1, v9, %[t1]\n\t"
+                    "vmacc.vx v0, %[t2], v10\n\t"
+                    "vmacc.vx v1, %[t3], v11\n\t"
+                    "vmacc.vx v0, %[t4], v12\n\t"
+                    "vmacc.vx v1, %[t5], v13\n\t"
+                    "vmacc.vx v0, %[t6], v14\n\t"
+                    "vmacc.vx v1, %[t7], v15\n\t"
                     "vmv.x.s %[tmp], v0\n\t"
-                    "add %[isum], %[isum], %[tmp]"
-                    : [tmp] "=&r" (tmp), [isum] "+&r" (isum)
+                    "vmv.x.s %[t1], v1\n\t"
+                    "add %[isum], %[isum], %[tmp]\n\t"
+                    "add %[isum], %[isum], %[t1]"
+                    : [tmp] "=&r" (tmp), [t1] "=&r" (t1), [t2] "=&r" (t2), [t3] "=&r" (t3)
+                    , [t4] "=&r" (t4), [t5] "=&r" (t5), [t6] "=&r" (t6), [t7] "=&r" (t7)
+                    , [isum] "+&r" (isum)
                     : [q2] "r" (q2), [scale] "r" (patmp), [q8] "r" (q8)
-                    , [vl32] "r" (32), [vl64] "r" (64), [vl128] "r" (128)
                     : "memory"
                     , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
                     , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
@@ -929,7 +975,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
             const  int8_t * restrict q8 = y[i].qs;
 
             int8_t * scale = (int8_t *)utmp;
-            int tmp;
+            int tmp, t1, t2, t3, t4, t5, t6, t7;
             __asm__ __volatile__(
                 "vsetivli zero, 12, e8, m1\n\t"
                 "vle8.v v0, (%[s6b])\n\t"
@@ -967,19 +1013,23 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
             int isum = 0;
             for (int j = 0; j < QK_K; j += 128) {
                 __asm__ __volatile__(
+                    "lb zero, 31(%[q3])\n\t"
                     "vsetvli zero, %[vl32], e8, m2, ta, mu\n\t"
                     "vle8.v v8, (%[q3])\n\t"
                     "vsrl.vi v10, v8, 2\n\t"
                     "vsrl.vi v12, v8, 4\n\t"
                     "vsrl.vi v14, v8, 6\n\t"
+                    "lb zero, 64(%[q8])\n\t"
                     "vand.vi v8, v8, 3\n\t"
                     "vand.vi v10, v10, 3\n\t"
                     "vand.vi v12, v12, 3\n\t"
                     "vle8.v v2, (%[qh])\n\t"
+                    "lb zero, 127(%[q8])\n\t"
                     "vand.vx v4, v2, %[m]\n\t"
                     "slli %[m], %[m], 1\n\t"
                     "vmseq.vx v0, v4, zero\n\t"
                     "vadd.vi v8, v8, -4, v0.t\n\t"
+                    "lb zero, 0(%[q8])\n\t"
                     "vand.vx v4, v2, %[m]\n\t"
                     "slli %[m], %[m], 1\n\t"
                     "vmseq.vx v0, v4, zero\n\t"
@@ -994,34 +1044,43 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
                     "vadd.vi v14, v14, -4, v0.t\n\t"
                     "vsetvli zero, %[vl128], e8, m8\n\t"
                     "vle8.v v0, (%[q8])\n\t"
+                    "lb %[tmp], 0(%[scale])\n\t"
+                    "lb %[t1], 1(%[scale])\n\t"
+                    "lb %[t2], 2(%[scale])\n\t"
+                    "lb %[t3], 3(%[scale])\n\t"
                     "vsetvli zero, %[vl64], e8, m4\n\t"
                     "vwmul.vv v16, v0, v8\n\t"
                     "vwmul.vv v24, v4, v12\n\t"
                     "vsetivli zero, 16, e16, m2\n\t"
                     "vmv.v.x v0, zero\n\t"
-                    "vwredsum.vs v10, v16, v0\n\t"
+                    "vwredsum.vs v8, v16, v0\n\t"
+                    "lb %[t4], 4(%[scale])\n\t"
+                    "lb %[t5], 5(%[scale])\n\t"
                     "vwredsum.vs v9, v18, v0\n\t"
-                    "vwredsum.vs v8, v20, v0\n\t"
-                    "vwredsum.vs v7, v22, v0\n\t"
-                    "vwredsum.vs v11, v24, v0\n\t"
-                    "vwredsum.vs v12, v26, v0\n\t"
-                    "vwredsum.vs v13, v28, v0\n\t"
-                    "vwredsum.vs v14, v30, v0\n\t"
+                    "vwredsum.vs v10, v20, v0\n\t"
+                    "vwredsum.vs v11, v22, v0\n\t"
+                    "vwredsum.vs v12, v24, v0\n\t"
+                    "lb %[t6], 6(%[scale])\n\t"
+                    "lb %[t7], 7(%[scale])\n\t"
+                    "vwredsum.vs v13, v26, v0\n\t"
+                    "vwredsum.vs v14, v28, v0\n\t"
+                    "vwredsum.vs v15, v30, v0\n\t"
                     "vsetivli zero, 4, e32, m1\n\t"
-                    "vslideup.vi v10, v9, 1\n\t"
-                    "vslideup.vi v8, v7, 1\n\t"
-                    "vslideup.vi v11, v12, 1\n\t"
-                    "vslideup.vi v13, v14, 1\n\t"
-                    "vslideup.vi v10, v8, 2\n\t"
-                    "vslideup.vi v11, v13, 2\n\t"
-                    "vsetivli zero, 8, e32, m2\n\t"
-                    "vle8.v v15, (%[scale])\n\t"
-                    "vsext.vf4 v12, v15\n\t"
-                    "vmul.vv v10, v10, v12\n\t"
-                    "vredsum.vs v0, v10, v0\n\t"
+                    "vmul.vx v0, v8, %[tmp]\n\t"
+                    "vmul.vx v1, v9, %[t1]\n\t"
+                    "vmacc.vx v0, %[t2], v10\n\t"
+                    "vmacc.vx v1, %[t3], v11\n\t"
+                    "vmacc.vx v0, %[t4], v12\n\t"
+                    "vmacc.vx v1, %[t5], v13\n\t"
+                    "vmacc.vx v0, %[t6], v14\n\t"
+                    "vmacc.vx v1, %[t7], v15\n\t"
                     "vmv.x.s %[tmp], v0\n\t"
-                    "add %[isum], %[isum], %[tmp]"
-                    : [tmp] "=&r" (tmp), [m] "+&r" (m), [isum] "+&r" (isum)
+                    "vmv.x.s %[t1], v1\n\t"
+                    "add %[isum], %[isum], %[tmp]\n\t"
+                    "add %[isum], %[isum], %[t1]"
+                    : [tmp] "=&r" (tmp), [t1] "=&r" (t1), [t2] "=&r" (t2), [t3] "=&r" (t3)
+                    , [t4] "=&r" (t4), [t5] "=&r" (t5), [t6] "=&r" (t6), [t7] "=&r" (t7)
+                    , [m] "+&r" (m), [isum] "+&r" (isum)
                     : [vl128] "r" (128), [vl64] "r" (64), [vl32] "r" (32)
                     , [q3] "r" (q3), [qh] "r" (qh), [scale] "r" (scale), [q8] "r" (q8)
                     : "memory"

ggml/src/ggml-cuda/CMakeLists.txt

@@ -124,6 +124,7 @@ if (CUDAToolkit_FOUND)
 
     if (GGML_CUDA_DEBUG)
         list(APPEND CUDA_FLAGS -lineinfo)
+        add_compile_definitions(GGML_CUDA_DEBUG)
     endif()
 
     if (CUDAToolkit_VERSION VERSION_GREATER_EQUAL "12.8")

ggml/src/ggml-cuda/cpy.cu

@@ -198,7 +198,7 @@ static void ggml_cpy_flt_cuda(
     if (transposed) {
         GGML_ASSERT(ne == ne00*ne01*ne02);  // ne[3] is 1 assumed
         int ne00n, ne01n, ne02n;
-        if (nb00 < nb02) {
+        if (nb00 <= nb02) { // most likely safe to handle nb00 = nb02 case here
             ne00n = ne00;
             ne01n = ne01;
             ne02n = ne02;
@@ -206,8 +206,6 @@ static void ggml_cpy_flt_cuda(
             ne00n = ne00;
             ne01n = ne01*ne02;
             ne02n = 1;
-        } else {
-            GGML_ASSERT(false);
         }
 
         dim3 dimGrid( (ne01n + CUDA_CPY_TILE_DIM_2D - 1) / CUDA_CPY_TILE_DIM_2D,

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -27,7 +27,6 @@
 #include "ggml-cuda/mmq.cuh"
 #include "ggml-cuda/mmvf.cuh"
 #include "ggml-cuda/mmvq.cuh"
-#include "ggml-cuda/moe-expert-reduce.cuh"
 #include "ggml-cuda/norm.cuh"
 #include "ggml-cuda/opt-step-adamw.cuh"
 #include "ggml-cuda/opt-step-sgd.cuh"
@@ -3152,8 +3151,6 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
 
             for (int i = 0; i < cgraph->n_nodes; i++) {
                 ggml_tensor * node = cgraph->nodes[i];
-
-
 #ifdef GGML_CUDA_DEBUG
                 const int nodes_fused = i - prev_i - 1;
                 prev_i = i;
@@ -3199,31 +3196,6 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
                         continue;
                     }
 
-                    if (node->op == GGML_OP_MUL) {
-                        int current_node = i + 1;
-                        int num_views    = 0;
-                        int num_adds     = 0;
-                        while (current_node < cgraph->n_nodes && cgraph->nodes[current_node]->op == GGML_OP_VIEW) {
-                            num_views++;
-                            current_node++;
-                        }
-
-                        while (current_node < cgraph->n_nodes && cgraph->nodes[current_node]->op == GGML_OP_ADD &&
-                                num_adds < num_views - 1) {
-                            num_adds++;
-                            current_node++;
-                        }
-
-                        if (num_adds == num_views - 1 && num_views > 0) {
-                            ggml_tensor * dst_node = cgraph->nodes[current_node - 1];
-                            if (ggml_cuda_should_use_moe_expert_reduce(cgraph, i, current_node)) {
-                                ggml_cuda_op_moe_expert_reduce(*cuda_ctx, node->src[0], node->src[1], dst_node);
-                                i += num_views + num_adds;
-                                continue;
-                            }
-                        }
-                    }
-
                     if (node->op == GGML_OP_ADD) {
                         int n_fuse = 0;
                         ggml_op ops[8];
@@ -3302,6 +3274,13 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
                                 continue;
                             }
 
+                            // we don't support repeating adds
+                            if (bias_op == GGML_OP_ADD &&
+                                (!ggml_are_same_shape(gate_bias_n->src[0], gate_bias_n->src[1]) ||
+                                 !ggml_are_same_shape(up_bias_n->src[0], up_bias_n->src[1]))) {
+                                continue;
+                            }
+
                             const ggml_tensor * src0 = up_n->src[0];
                             const ggml_tensor * src1 = up_n->src[1];
                             const ggml_tensor * ids  = up_n->src[2];
@@ -3411,6 +3390,10 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
                             continue;
                         }
 
+                        if (bias_op == GGML_OP_ADD && !ggml_are_same_shape(bias_node->src[0], bias_node->src[1])) {
+                            continue;
+                        }
+
                         ggml_cuda_mm_fusion_args_host fusion_data{};
                         fusion_data.x_bias = bias_tensor;
 

ggml/src/ggml-cuda/mmf.cu

@@ -129,7 +129,13 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const
     if (src0_ne[0] % (warp_size * (4/ts)) != 0) {
         return false;
     }
-    for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
+
+    if (src0_nb[0] != ts) {
+        return false;
+    }
+
+    // Pointers not aligned to the size of half2/nv_bfloat162/float2 would result in a crash:
+    for (size_t i = 1; i < GGML_MAX_DIMS; ++i) {
         if (src0_nb[i] % (2*ts) != 0) {
             return false;
         }

ggml/src/ggml-cuda/mmq.cuh

@@ -3494,7 +3494,7 @@ static __global__ void mul_mat_q_stream_k_fixup(
     const int col_diff = col_high - col_low;
 
     for (int j = threadIdx.y*warp_size + threadIdx.x; j < mmq_x; j += nwarps*warp_size) {
-        ids_dst_shared[j] = ids_dst[col_low + j];
+        ids_dst_shared[j] = ids_dst[col_low + jt*mmq_x + j];
     }
     __syncthreads();
 

ggml/src/ggml-cuda/mmvf.cu

@@ -720,12 +720,19 @@ bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0
     if (src0_ne[0] % 2 != 0) {
         return false;
     }
+
     const size_t ts = ggml_type_size(type);
-    for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
+    if (src0_nb[0] != ts) {
+        return false;
+    }
+
+    // Pointers not aligned to the size of half2/nv_bfloat162/float2 would result in a crash:
+    for (size_t i = 1; i < GGML_MAX_DIMS; ++i) {
         if (src0_nb[i] % (2*ts) != 0) {
             return false;
         }
     }
+
     switch (type) {
         case GGML_TYPE_F32:
             if (GGML_CUDA_CC_IS_NVIDIA(cc)) {

ggml/src/ggml-cuda/moe-expert-reduce.cu

@@ -1,168 +0,0 @@
-#include "moe-expert-reduce.cuh"
-
-// This kernel is a fusion of the expert weight reduce, common in MoE models
-
-template <int n_expert_used_template>
-__global__ void moe_expert_reduce_cuda(const float * __restrict__ experts,
-                                       const float * __restrict__ weights,
-                                       float * __restrict__ dst,
-                                       const int n_expert_used,
-                                       const int n_cols) {
-    const int row = blockIdx.x;
-    const int col = blockIdx.y * blockDim.x + threadIdx.x;
-    if (col >= n_cols) {
-        return;
-    }
-
-    experts += row * n_cols * n_expert_used;
-    weights += row * n_expert_used;
-    dst += row * n_cols;
-
-    float acc = 0.f;
-    if constexpr (n_expert_used_template == 0) {
-        for (int expert = 0; expert < n_expert_used; ++expert) {
-            ggml_cuda_mad(acc, experts[col], weights[expert]);
-            experts += n_cols;
-        }
-        dst[col] = acc;
-    } else {
-#pragma unroll
-        for (int i = 0; i < n_expert_used_template; ++i) {
-            ggml_cuda_mad(acc, experts[col], weights[i]);
-            experts += n_cols;
-        }
-        dst[col] = acc;
-    }
-}
-
-static void launch_moe_expert_reduce(ggml_backend_cuda_context & ctx,
-                                     const float *               experts,
-                                     const float *               weights,
-                                     float *                     dst,
-                                     const int                   n_expert_used,
-                                     const int                   n_cols,
-                                     const int                   n_rows) {
-    const int block_size = 32;
-
-    const int n_blocks_x = n_rows;
-    const int n_blocks_y = (n_cols + block_size - 1) / block_size;
-
-    dim3 block_dims(block_size);
-    dim3 grid_dims(n_blocks_x, n_blocks_y);
-
-    cudaStream_t stream = ctx.stream();
-    switch (n_expert_used) {
-        case 1:
-            moe_expert_reduce_cuda<1>
-                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
-            break;
-        case 2:
-            moe_expert_reduce_cuda<2>
-                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
-            break;
-        case 4:
-            moe_expert_reduce_cuda<4>
-                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
-            break;
-        case 6:
-            moe_expert_reduce_cuda<6>
-                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
-            break;
-        case 8:
-            moe_expert_reduce_cuda<8>
-                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
-            break;
-        case 16:
-            moe_expert_reduce_cuda<16>
-                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
-            break;
-        case 32:
-            moe_expert_reduce_cuda<32>
-                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
-            break;
-        case 64:
-            moe_expert_reduce_cuda<64>
-                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
-            break;
-        case 128:
-            moe_expert_reduce_cuda<128>
-                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
-            break;
-        default:
-            moe_expert_reduce_cuda<0>
-                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
-            break;
-    }
-}
-
-bool ggml_cuda_should_use_moe_expert_reduce(const ggml_cgraph * cgraph, int start_index, int end_index) {
-    const ggml_tensor * mul = cgraph->nodes[start_index];
-
-    if (mul->op != GGML_OP_MUL || !ggml_is_contiguous(mul->src[0]) || !ggml_is_contiguous(mul->src[1])) {
-        return false;
-    }
-
-    int    current_node   = start_index + 1;
-    size_t current_offset = 0;
-
-    std::vector<const ggml_tensor *> view_nodes;
-    //check if all are views of the expert in increasing order
-    while (current_node < end_index && cgraph->nodes[current_node]->op == GGML_OP_VIEW) {
-        const ggml_tensor * node = cgraph->nodes[current_node];
-        if (node->view_src != mul) {
-            return false;
-        }
-        if (node->view_offs < current_offset) {
-            return false;
-        }
-        current_offset = node->view_offs;
-        current_node++;
-        view_nodes.push_back(node);
-    }
-
-    //check if all the adds are in increasing order
-    const ggml_tensor * prev_add_src = view_nodes.empty() ? nullptr : view_nodes[0];
-    int                 num_adds     = 0;
-    int                 num_views    = view_nodes.size();
-    while (current_node < end_index && cgraph->nodes[current_node]->op == GGML_OP_ADD) {
-        const ggml_tensor * add_node = cgraph->nodes[current_node];
-
-        bool is_first_op_ok  = num_views > num_adds ? add_node->src[0] == prev_add_src : false;
-        bool is_second_op_ok = num_views > num_adds ? add_node->src[1] == view_nodes[num_adds + 1] : false;
-
-        if (!is_first_op_ok || !is_second_op_ok) {
-            return false;
-        }
-        prev_add_src = add_node;
-
-        num_adds++;
-        current_node++;
-    }
-
-    if (num_views != num_adds + 1) {
-        return false;
-    }
-
-    return true;
-}
-
-void ggml_cuda_op_moe_expert_reduce(ggml_backend_cuda_context & ctx,
-                                    const ggml_tensor *         experts,
-                                    const ggml_tensor *         weights,
-                                    ggml_tensor *               dst) {
-    const int n_rows        = experts->ne[2];
-    const int n_expert_used = experts->ne[1];
-    const int n_cols        = experts->ne[0];
-
-    GGML_ASSERT(experts->type == GGML_TYPE_F32);
-    GGML_ASSERT(weights->type == GGML_TYPE_F32);
-    GGML_ASSERT(ggml_is_contiguous(experts));
-    GGML_ASSERT(ggml_is_contiguous(weights));
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-
-    const float * experts_d = (const float *) experts->data;
-    const float * weights_d = (const float *) weights->data;
-    float *       dst_d     = (float *) dst->data;
-
-    launch_moe_expert_reduce(ctx, experts_d, weights_d, dst_d, n_expert_used, n_cols, n_rows);
-}

ggml/src/ggml-cuda/moe-expert-reduce.cuh

@@ -1,11 +0,0 @@
-#include "common.cuh"
-#include "ggml.h"
-
-#include <initializer_list>
-
-void ggml_cuda_op_moe_expert_reduce(ggml_backend_cuda_context & ctx,
-                                    const ggml_tensor *         experts,
-                                    const ggml_tensor *         weights,
-                                    ggml_tensor *               dst);
-
-bool ggml_cuda_should_use_moe_expert_reduce(const ggml_cgraph * cgraph, int start_index, int end_index);

ggml/src/ggml-cuda/upscale.cu

@@ -81,6 +81,70 @@ static __global__ void upscale_f32_bilinear(const float * x, float * dst,
     dst[index] = result;
 }
 
+namespace bicubic_interpolation {
+// https://en.wikipedia.org/wiki/Bicubic_interpolation#Bicubic_convolution_algorithm
+__device__ const float a = -0.75f; // use alpha = -0.75 (same as PyTorch)
+
+static __device__ float weight1(float x) { return ((a + 2) * x - (a + 3)) * x * x + 1; };
+static __device__ float weight2(float x) { return ((a * x - 5 * a) * x + 8 * a) * x - 4 * a; };
+
+static __device__ float bicubic(float p0, float p1, float p2, float p3, float x) {
+    const float w0 = weight2(x + 1);
+    const float w1 = weight1(x + 0);
+    const float w2 = weight1(1 - x);
+    const float w3 = weight2(2 - x);
+    return p0 * w0 + p1 * w1 + p2 * w2 + p3 * w3;
+};
+} // namespace bicubic_interpolation
+
+static __global__ void upscale_f32_bicubic(const float * x, float * dst,
+        const int nb00, const int nb01, const int nb02, const int nb03,
+        const int ne00_src, const int ne01_src,
+        const int ne10_dst, const int ne11_dst, const int ne12_dst, const int ne13_dst,
+        const float sf0, const float sf1, const float sf2, const float sf3,
+        const float pixel_offset) {
+    using bicubic_interpolation::bicubic;
+
+    const int64_t index              = threadIdx.x + blockIdx.x * blockDim.x;
+    const int64_t dst_total_elements = ne10_dst * ne11_dst * ne12_dst * ne13_dst;
+
+    if (index >= dst_total_elements) {
+        return;
+    }
+
+    const int i10_dst = index % ne10_dst;
+    const int i11_dst = (index / ne10_dst) % ne11_dst;
+    const int i12_dst = (index / (ne10_dst * ne11_dst)) % ne12_dst;
+    const int i13_dst = index / (ne10_dst * ne11_dst * ne12_dst);
+
+    const int i02_src = (int)(i12_dst / sf2);
+    const int i03_src = (int)(i13_dst / sf3);
+
+    const float y_src_f = ((float)i11_dst + pixel_offset) / sf1 - pixel_offset;
+    const int y0_src    = (int)floorf(y_src_f);
+    const float dy      = y_src_f - (float)y0_src;
+
+    const float x_src_f = ((float)i10_dst + pixel_offset) / sf0 - pixel_offset;
+    const int x0_src    = (int)floorf(x_src_f);
+    const float dx      = x_src_f - (float)x0_src;
+
+    const char * x_base = (const char *)x + (int64_t)i02_src * nb02 + (int64_t)i03_src * nb03;
+
+    auto load = [=](int x_off, int y_off) -> float {
+        int i00_src = max(0, min(x0_src + x_off, ne00_src - 1));
+        int i01_src = max(0, min(y0_src + y_off, ne01_src - 1));
+        return *(const float *)(x_base + (int64_t)i00_src * nb00 + (int64_t)i01_src * nb01);
+    };
+
+    const float result = bicubic(
+        bicubic(load(-1,-1), load(0,-1), load(1,-1), load(2,-1), dx),
+        bicubic(load(-1, 0), load(0, 0), load(1, 0), load(2, 0), dx),
+        bicubic(load(-1, 1), load(0, 1), load(1, 1), load(2, 1), dx),
+        bicubic(load(-1, 2), load(0, 2), load(1, 2), load(2, 2), dx), dy);
+
+    dst[index] = result;
+}
+
 static void upscale_f32_cuda(const float * x, float * dst,
         const int nb00, const int nb01, const int nb02, const int nb03,
         const int ne10, const int ne11, const int ne12, const int ne13,
@@ -104,6 +168,18 @@ static void upscale_f32_bilinear_cuda(const float * x, float * dst,
     upscale_f32_bilinear<<<num_blocks, CUDA_UPSCALE_BLOCK_SIZE,0,stream>>>(x, dst, nb00, nb01, nb02, nb03, ne00_src, ne01_src, ne10_dst, ne11_dst, ne12_dst, ne13_dst, sf0, sf1, sf2, sf3, pixel_offset);
 }
 
+static void upscale_f32_bicubic_cuda(const float * x, float * dst,
+        const int nb00, const int nb01, const int nb02, const int nb03,
+        const int ne00_src, const int ne01_src,
+        const int ne10_dst, const int ne11_dst, const int ne12_dst, const int ne13_dst,
+        const float sf0, const float sf1, const float sf2, const float sf3,
+        const float pixel_offset, cudaStream_t stream) {
+    const int64_t dst_size   = ne10_dst * ne11_dst * ne12_dst * ne13_dst;
+    const int64_t num_blocks = (dst_size + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE;
+
+    upscale_f32_bicubic<<<num_blocks, CUDA_UPSCALE_BLOCK_SIZE,0,stream>>>(x, dst, nb00, nb01, nb02, nb03, ne00_src, ne01_src, ne10_dst, ne11_dst, ne12_dst, ne13_dst, sf0, sf1, sf2, sf3, pixel_offset);
+}
+
 void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const float * src0_d = (const float *)src0->data;
@@ -121,17 +197,22 @@ void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float sf2 = (float)dst->ne[2]/src0->ne[2];
     const float sf3 = (float)dst->ne[3]/src0->ne[3];
 
+    float pixel_offset = 0.5f;
+    if (mode_flags & GGML_SCALE_FLAG_ALIGN_CORNERS) {
+        sf0          = dst->ne[0] > 1 && src0->ne[0] > 1 ? (float)(dst->ne[0] - 1) / (src0->ne[0] - 1) : sf0;
+        sf1          = dst->ne[1] > 1 && src0->ne[1] > 1 ? (float)(dst->ne[1] - 1) / (src0->ne[1] - 1) : sf1;
+        pixel_offset = 0.0f;
+    }
+
     if (mode == GGML_SCALE_MODE_NEAREST) {
         upscale_f32_cuda(src0_d, dst_d, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], sf0, sf1, sf2, sf3, stream);
     } else if (mode == GGML_SCALE_MODE_BILINEAR) {
-        float pixel_offset = 0.5f;
-        if (mode_flags & GGML_SCALE_FLAG_ALIGN_CORNERS) {
-            sf0          = dst->ne[0] > 1 && src0->ne[0] > 1 ? (float)(dst->ne[0] - 1) / (src0->ne[0] - 1) : sf0;
-            sf1          = dst->ne[1] > 1 && src0->ne[1] > 1 ? (float)(dst->ne[1] - 1) / (src0->ne[1] - 1) : sf1;
-            pixel_offset = 0.0f;
-        }
         upscale_f32_bilinear_cuda(src0_d, dst_d, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
                                  src0->ne[0], src0->ne[1], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
                                  sf0, sf1, sf2, sf3, pixel_offset, stream);
+    } else if (mode == GGML_SCALE_MODE_BICUBIC) {
+        upscale_f32_bicubic_cuda(src0_d, dst_d, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
+                                 src0->ne[0], src0->ne[1], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
+                                 sf0, sf1, sf2, sf3, pixel_offset, stream);
     }
 }

ggml/src/ggml-metal/ggml-metal-context.m

@@ -289,7 +289,7 @@ void ggml_metal_set_tensor_async(ggml_metal_t ctx, struct ggml_tensor * tensor,
 
         // queue the copy operation into the queue of the Metal context
         // this will be queued at the end, after any currently ongoing GPU operations
-        id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBufferWithUnretainedReferences];
+        id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBuffer];
         id<MTLBlitCommandEncoder> encoder = [cmd_buf blitCommandEncoder];
 
         [encoder copyFromBuffer:buf_src
@@ -300,6 +300,7 @@ void ggml_metal_set_tensor_async(ggml_metal_t ctx, struct ggml_tensor * tensor,
 
         [encoder endEncoding];
         [cmd_buf commit];
+        [buf_src release];
 
         // do not wait here for completion
         //[cmd_buf waitUntilCompleted];
@@ -330,7 +331,7 @@ void ggml_metal_get_tensor_async(ggml_metal_t ctx, const struct ggml_tensor * te
 
         // queue the copy operation into the queue of the Metal context
         // this will be queued at the end, after any currently ongoing GPU operations
-        id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBufferWithUnretainedReferences];
+        id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBuffer];
         id<MTLBlitCommandEncoder> encoder = [cmd_buf blitCommandEncoder];
 
         [encoder copyFromBuffer:bid_src.metal
@@ -341,6 +342,7 @@ void ggml_metal_get_tensor_async(ggml_metal_t ctx, const struct ggml_tensor * te
 
         [encoder endEncoding];
         [cmd_buf commit];
+        [buf_dst release];
 
         // do not wait here for completion
         //[cmd_buf waitUntilCompleted];

ggml/src/ggml-opencl/ggml-opencl.cpp

@@ -2944,8 +2944,11 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
             return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; // Assuming F32 for now, can be expanded
         case GGML_OP_PAD:
             return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
-        case GGML_OP_UPSCALE:
-            return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
+        case GGML_OP_UPSCALE: {
+            ggml_scale_mode mode = (ggml_scale_mode)(ggml_get_op_params_i32(op, 0) & 0xFF);
+            return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32 &&
+                   (mode == GGML_SCALE_MODE_NEAREST || mode == GGML_SCALE_MODE_BILINEAR);
+        }
         case GGML_OP_CONV_2D:
             return (op->src[0]->type == GGML_TYPE_F16 && op->src[1]->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F16) ||
                    (op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32) ||

ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp

@@ -62,14 +62,8 @@ layout(push_constant) uniform parameter {
     uint32_t nb3;
 
     // fastdiv helper values
-    uint32_t KWmp;   uint32_t KWL;
-    uint32_t KWKHmp; uint32_t KWKHL;
     uint32_t OWmp;   uint32_t OWL;
     uint32_t OWOHmp; uint32_t OWOHL;
-#ifdef TRANSPOSE
-    uint32_t s0mp; uint32_t s0L;
-    uint32_t s1mp; uint32_t s1L;
-#endif
 }
 
 p;
@@ -84,6 +78,15 @@ layout(constant_id = 4) const uint TS_K            = 8;
 layout(constant_id = 5) const uint use_collectives = 1;
 layout(constant_id = 6) const uint SHMEM_PAD       = 4;
 
+layout(constant_id = 7)  const uint s0             = 1;
+layout(constant_id = 8)  const uint s1             = 1;
+layout(constant_id = 9)  const uint p0             = 0;
+layout(constant_id = 10) const uint p1             = 0;
+layout(constant_id = 11) const uint d0             = 1;
+layout(constant_id = 12) const uint d1             = 1;
+layout(constant_id = 13) const uint KW             = 1;
+layout(constant_id = 14) const uint KH             = 1;
+
 uint32_t       tid     = gl_LocalInvocationID.x;
 const uint32_t WG_SIZE = gl_WorkGroupSize.x;
 
@@ -92,7 +95,7 @@ uint splitWork(uint work_size, uint block_size) {
 }
 
 uint32_t K   = p.Cout;
-uint32_t CRS = p.Cin * p.KH * p.KW;
+uint32_t CRS = p.Cin * KH * KW;
 uint32_t NPQ = p.N * p.OH * p.OW;
 
 uint32_t n_elems_out = K * NPQ;
@@ -187,7 +190,7 @@ void main() {
     }
 #endif
     /* Advance block in CRS dim */
-    for (uint32_t B_idx_CRS = 0; B_idx_CRS < NB_CRS; B_idx_CRS++) {
+    [[dont_unroll]] for (uint32_t B_idx_CRS = 0; B_idx_CRS < NB_CRS; B_idx_CRS++) {
         uint32_t CRS_idx_a;
         uint32_t Cin_idx_a;
         uint32_t KH_idx_a;
@@ -200,10 +203,10 @@ void main() {
         uint32_t cached_KW_idx;
         if (use_collectives == 1) {
             cached_CRS_idx                = B_idx_CRS * BS_CRS + gl_SubgroupInvocationID;
-            cached_Cin_idx                = fastdiv(cached_CRS_idx, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH);
-            uint32_t cached_CRS_remainder = (cached_CRS_idx - cached_Cin_idx * p.KW * p.KH);
-            cached_KH_idx                 = fastdiv(cached_CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
-            cached_KW_idx                 = cached_CRS_remainder - cached_KH_idx * p.KW;
+            cached_Cin_idx                = cached_CRS_idx / (KW * KH);
+            uint32_t cached_CRS_remainder = cached_CRS_idx % (KW * KH);
+            cached_KH_idx                 = cached_CRS_remainder / KW;
+            cached_KW_idx                 = cached_CRS_remainder % KW;
 
             CRS_idx_a = subgroupShuffle(cached_CRS_idx, Ac);
             Cin_idx_a = subgroupShuffle(cached_Cin_idx, Ac);
@@ -211,21 +214,21 @@ void main() {
             KW_idx_a  = subgroupShuffle(cached_KW_idx, Ac);
         } else {
             CRS_idx_a              = B_idx_CRS * BS_CRS + Ac;  // Global CRS_idx_a (column index of A)
-            Cin_idx_a              = fastdiv(CRS_idx_a, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH);
-            uint32_t CRS_remainder = CRS_idx_a - Cin_idx_a * p.KW * p.KH;
-            KH_idx_a               = fastdiv(CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
-            KW_idx_a               = CRS_remainder - KH_idx_a * p.KW;
+            Cin_idx_a              = CRS_idx_a / (KW * KH);
+            uint32_t CRS_remainder = CRS_idx_a % (KW * KH);
+            KH_idx_a               = CRS_remainder / KW;
+            KW_idx_a               = CRS_remainder % KW;
         }
 #else
         CRS_idx_a     = B_idx_CRS * BS_CRS + Ac;  // Global CRS_idx_a (column index of A)
-        Cin_idx_a     = fastdiv(CRS_idx_a, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH); / (p.KW * p.KH);
-        CRS_remainder = CRS_idx_a - Cin_idx_a * p.KW * p.KH;
-        KH_idx_a      = fastdiv(CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
-        KW_idx_a      = CRS_remainder - KH_idx_a * p.KW;
+        Cin_idx_a     = CRS_idx_a / (KW * KH);
+        CRS_remainder = CRS_idx_a % (KW * KH);
+        KH_idx_a      = CRS_remainder / KW;
+        KW_idx_a      = CRS_remainder % KW;
 #endif
 
         /* Load kernel to A_block: (BS_K x BS_CRS)*/
-        for (uint32_t r_offset = 0; r_offset < BS_K; r_offset += ArpWg) {
+        UNROLL for (uint32_t r_offset = 0; r_offset < BS_K; r_offset += ArpWg) {
             uint32_t B_ly    = r_offset + Ar;
             uint32_t B_lx    = Ac;
             uint32_t K_idx   = B_idx_K * BS_K + B_ly; /* Global K_idx (row index of A)*/
@@ -262,27 +265,27 @@ void main() {
                 KW_idx_b  = subgroupShuffle(cached_KW_idx, r_offset + Br);
             } else {
                 CRS_idx_b              = B_idx_CRS * BS_CRS + B_ly; /* Global CRS index (row index of B) */
-                Cin_idx_b              = fastdiv(CRS_idx_b, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH);
-                uint32_t CRS_remainder = CRS_idx_b - Cin_idx_b * p.KW * p.KH;
-                KH_idx_b               = fastdiv(CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
-                KW_idx_b               = CRS_remainder - KH_idx_b * p.KW;
+                Cin_idx_b              = CRS_idx_b / (KW * KH);
+                uint32_t CRS_remainder = CRS_idx_b % (KW * KH);
+                KH_idx_b               = CRS_remainder / KW;
+                KW_idx_b               = CRS_remainder % KW;
             }
 #else
             CRS_idx_b              = B_idx_CRS * BS_CRS + B_ly; /* Global CRS index (row index of B) */
-            Cin_idx_b              = fastdiv(CRS_idx_b, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH);
-            uint32_t CRS_remainder = CRS_idx_b - Cin_idx_b * p.KW * p.KH;
-            KH_idx_b               = fastdiv(CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
-            KW_idx_b               = CRS_remainder - KH_idx_b * p.KW;
+            Cin_idx_b              = CRS_idx_b / (KW * KH);
+            uint32_t CRS_remainder = CRS_idx_b % (KW * KH);
+            KH_idx_b               = CRS_remainder / KW;
+            KW_idx_b               = CRS_remainder % KW;
 #endif
 
 #ifdef TRANSPOSE
-            uint32_t H_idx_x_s1 = OH_idx - KH_idx_b * p.d1 + p.p1;
-            uint32_t W_idx_x_s0 = OW_idx - KW_idx_b * p.d0 + p.p0;
-            uint32_t H_idx = fastdiv(H_idx_x_s1, p.s1mp, p.s1L);
-            uint32_t W_idx = fastdiv(W_idx_x_s0, p.s0mp, p.s0L);
+            uint32_t H_idx_x_s1 = OH_idx - KH_idx_b * d1 + p1;
+            uint32_t W_idx_x_s0 = OW_idx - KW_idx_b * d0 + p0;
+            uint32_t H_idx = H_idx_x_s1 / s1;
+            uint32_t W_idx = W_idx_x_s0 / s0;
 #else
-            uint32_t H_idx = OH_idx * p.s1 + KH_idx_b * p.d1 - p.p1;
-            uint32_t W_idx = OW_idx * p.s0 + KW_idx_b * p.d0 - p.p0;
+            uint32_t H_idx = OH_idx * s1 + KH_idx_b * d1 - p1;
+            uint32_t W_idx = OW_idx * s0 + KW_idx_b * d0 - p0;
 #endif
             uint32_t src_idx =
                 min(max(W_idx + H_idx * p.nb11 + Cin_idx_b * p.nb12 + N_idx * p.nb13, 0), p.Cin * p.N * p.W * p.H - 1);
@@ -290,7 +293,7 @@ void main() {
             if (CRS_idx_b >= CRS || NPQ_idx >= NPQ
                 || H_idx >= p.H || W_idx >= p.W // Lower bound checks aren't necessary. (idx >= 0x80000000 for such case)
 #ifdef TRANSPOSE
-                || (H_idx_x_s1 - H_idx * p.s1 != 0) || (W_idx_x_s0 - W_idx * p.s0 != 0)
+                || (H_idx_x_s1 - H_idx * s1 != 0) || (W_idx_x_s0 - W_idx * s0 != 0)
 #endif
                 ) {
                 val = 0.0;

ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.glsl

@@ -3,6 +3,9 @@
 
 #include "rte.glsl"
 #include "utils.glsl"
+#if RMS_NORM_ROPE_FUSION
+#include "rope_params.glsl"
+#endif
 
 layout (push_constant) uniform parameter
 {
@@ -12,11 +15,16 @@ layout (push_constant) uniform parameter
     uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23;
     uint misalign_offsets;
     float param1; float param2; int param3;
+#if RMS_NORM_ROPE_FUSION
+    rope_params rope;
+#endif
 } p;
 
+#if !RMS_NORM_ROPE_FUSION
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
 layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
 layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
+#endif
 
 // true if src0/src1 are the same shape and the indices can be reused without additional modulus
 layout(constant_id = 0) const bool norepeat = false;

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.glsl

@@ -49,6 +49,7 @@ layout (push_constant) uniform parameter
     uint batch_stride_d;
 
     uint enable_bias;
+    uint enable_scale;
 
 #ifdef MUL_MAT_ID
     uint nei0;
@@ -129,6 +130,12 @@ void reduce_result(inout FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t
                     temp[j][n] += FLOAT_TYPE(data_bias[j*p.batch_stride_d + d_offset + first_row + n]);
 #endif
                 }
+#ifdef MUL_MAT_ID
+                if (p.enable_scale != 0) {
+                    const uint expert_idx = gl_GlobalInvocationID.y;
+                    temp[j][n] *= FLOAT_TYPE(data_bias[expert_idx]);
+                }
+#endif
                 data_d[j*p.batch_stride_d + d_offset + first_row + n] = D_TYPE(temp[j][n]);
             }
         }
@@ -171,6 +178,12 @@ void reduce_result(FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t d_offs
                     temp[j][n] += FLOAT_TYPE(data_bias[j*p.batch_stride_d + d_offset + first_row + n]);
 #endif
                 }
+#ifdef MUL_MAT_ID
+                if (p.enable_scale != 0) {
+                    const uint expert_idx = gl_GlobalInvocationID.y;
+                    temp[j][n] *= FLOAT_TYPE(data_bias[expert_idx]);
+                }
+#endif
                 data_d[j*p.batch_stride_d + d_offset + first_row + n] = D_TYPE(temp[j][n]);
             }
         }
@@ -203,6 +216,12 @@ void reduce_result(FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t d_offs
                     tmpsh[j][n][0] += FLOAT_TYPE(data_bias[j*p.batch_stride_d + d_offset + first_row + n]);
 #endif
                 }
+#ifdef MUL_MAT_ID
+                if (p.enable_scale != 0) {
+                    const uint expert_idx = gl_GlobalInvocationID.y;
+                    tmpsh[j][n][0] *= FLOAT_TYPE(data_bias[expert_idx]);
+                }
+#endif
                 data_d[j*p.batch_stride_d + d_offset + first_row + n] = D_TYPE(tmpsh[j][n][0]);
             }
         }

ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp

@@ -100,7 +100,6 @@ layout (push_constant) uniform parameter
 layout (constant_id = 0) const uint BLOCK_SIZE = 64;
 layout (constant_id = 1) const uint BM = 64;
 layout (constant_id = 2) const uint BN = 64;
-layout (constant_id = 3) const uint BK = 16;  // Assumed to be 32 if working with a quant
 layout (constant_id = 4) const uint WM = 32;
 layout (constant_id = 5) const uint WN = 32;
 layout (constant_id = 6) const uint WMITER = 2;
@@ -109,6 +108,14 @@ layout (constant_id = 8) const uint TN = 2;
 layout (constant_id = 9) const uint TK = 1;  // Only needed for coopmat
 layout (constant_id = 10) const uint WARP = 32;
 
+#if defined(DATA_A_F32) || defined(DATA_A_F16)
+#define BK 32
+#define BK_STEP 4
+#else
+layout (constant_id = 3) const uint BK = 16;  // Assumed to be 32 if working with a quant
+#define BK_STEP 2
+#endif
+
 #ifdef COOPMAT
 #define SHMEM_STRIDE (BK / 2 + 4)
 #else
@@ -244,8 +251,13 @@ void main() {
     }
 #else
     ACC_TYPE_VEC2 sums[WMITER * TM * WNITER * TN/2];
+#if defined(DATA_A_F32) || defined(DATA_A_F16)
+    FLOAT_TYPE_VEC4 cache_a[WMITER * TM];
+    FLOAT_TYPE_VEC4 cache_b;
+#else
     FLOAT_TYPE_VEC2 cache_a[WMITER * TM];
     FLOAT_TYPE_VEC2 cache_b;
+#endif
 
     [[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN/2; i++) {
         sums[i] = ACC_TYPE_VEC2(0.0f, 0.0f);
@@ -283,24 +295,41 @@ void main() {
             }
         }
 #else
-        [[unroll]] for (uint i = 0; i < BK / 2; i++) {
+        [[unroll]] for (uint i = 0; i < BK / BK_STEP; i++) {
             // Load from shared into cache
             [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
                 [[unroll]] for (uint j = 0; j < TM; j++) {
+                #if defined(DATA_A_F32) || defined(DATA_A_F16)
+                    cache_a[wsir * TM + j].xy = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * SHMEM_STRIDE + 2 * i    ];
+                    cache_a[wsir * TM + j].zw = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * SHMEM_STRIDE + 2 * i + 1];
+                #else
                     cache_a[wsir * TM + j] = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * SHMEM_STRIDE + i];
+                #endif
                 }
             }
 
             [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) {
                 [[unroll]] for (uint cc = 0; cc < TN; cc++) {
+                #if defined(DATA_A_F32) || defined(DATA_A_F16)
+                    cache_b.xy = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + cc) * SHMEM_STRIDE + 2 * i    ];
+                    cache_b.zw = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + cc) * SHMEM_STRIDE + 2 * i + 1];
+                #else
                     cache_b = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + cc) * SHMEM_STRIDE + i];
+                #endif
 
                     [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
                         [[unroll]] for (uint cr = 0; cr < TM / 2; cr++) {
                             // [WNITER][TN][WMITER][TM / 2] -> [wsic][cc][wsir][cr]
                             const uint sums_idx = (wsic * TN + cc) * WMITER * (TM / 2) + wsir * (TM / 2) + cr;
+                        #if defined(DATA_A_F32) || defined(DATA_A_F16)
+                            sums[sums_idx].x = fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr    ].x), ACC_TYPE(cache_b.x), fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr    ].y), ACC_TYPE(cache_b.y),
+                                               fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr    ].z), ACC_TYPE(cache_b.z), fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr    ].w), ACC_TYPE(cache_b.w), sums[sums_idx].x))));
+                            sums[sums_idx].y = fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr + 1].x), ACC_TYPE(cache_b.x), fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr + 1].y), ACC_TYPE(cache_b.y),
+                                               fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr + 1].z), ACC_TYPE(cache_b.z), fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr + 1].w), ACC_TYPE(cache_b.w), sums[sums_idx].y))));
+                        #else
                             sums[sums_idx].x = fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr    ].x), ACC_TYPE(cache_b.x), fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr    ].y), ACC_TYPE(cache_b.y), sums[sums_idx].x));
                             sums[sums_idx].y = fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr + 1].x), ACC_TYPE(cache_b.x), fma(ACC_TYPE(cache_a[wsir * TM + 2 * cr + 1].y), ACC_TYPE(cache_b.y), sums[sums_idx].y));
+                        #endif
                         }
                     }
                 }

ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq.comp

@@ -211,7 +211,9 @@ void main() {
             const uint iqs = loadr_a;
 
             [[unroll]] for (uint k_step = 0; k_step < BK_STEP; k_step++) {
-                block_a_to_shmem(k_step * BM + buf_ib, ib + k_step, iqs);
+                if (block + k_step * BK < end_k) {
+                    block_a_to_shmem(k_step * BM + buf_ib, ib + k_step, iqs);
+                }
             }
         }
         [[unroll]] for (uint l = 0; loadc_b + l < BN; l += loadstride_b) {
@@ -226,7 +228,7 @@ void main() {
             const uint iqs = loadr_b;
 
             [[unroll]] for (uint k_step = 0; k_step < BK_STEP; k_step++) {
-                block_b_to_shmem(k_step * BN + buf_ib, ib + k_step, iqs);
+                block_b_to_shmem(k_step * BN + buf_ib, ib + k_step, iqs, block + k_step * BK < end_k);
             }
         }
 

ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.glsl

@@ -469,19 +469,30 @@ ACC_TYPE mmq_dot_product(const uint ib_a) {
 #endif
 
 #ifdef MMQ_SHMEM
-void block_b_to_shmem(const uint buf_ib, const uint ib, const uint iqs) {
-    const uint ib_outer = ib / 4;
-    const uint ib_inner = ib % 4;
+void block_b_to_shmem(const uint buf_ib, const uint ib, const uint iqs, const bool is_in_bounds) {
+    if (is_in_bounds) {
+        const uint ib_outer = ib / 4;
+        const uint ib_inner = ib % 4;
 
-    if (iqs == 0) {
-        buf_b[buf_ib].ds = FLOAT_TYPE_VEC2(data_b[ib_outer].ds[ib_inner]);
+        if (iqs == 0) {
+            buf_b[buf_ib].ds = FLOAT_TYPE_VEC2(data_b[ib_outer].ds[ib_inner]);
+        }
+
+        const ivec4 values = data_b[ib_outer].qs[ib_inner * 2 + iqs];
+        buf_b[buf_ib].qs[iqs * 4    ] = values.x;
+        buf_b[buf_ib].qs[iqs * 4 + 1] = values.y;
+        buf_b[buf_ib].qs[iqs * 4 + 2] = values.z;
+        buf_b[buf_ib].qs[iqs * 4 + 3] = values.w;
+    } else {
+        if (iqs == 0) {
+            buf_b[buf_ib].ds = FLOAT_TYPE_VEC2(0.0f);
+        }
+
+        buf_b[buf_ib].qs[iqs * 4    ] = 0;
+        buf_b[buf_ib].qs[iqs * 4 + 1] = 0;
+        buf_b[buf_ib].qs[iqs * 4 + 2] = 0;
+        buf_b[buf_ib].qs[iqs * 4 + 3] = 0;
     }
-
-    const ivec4 values = data_b[ib_outer].qs[ib_inner * 2 + iqs];
-    buf_b[buf_ib].qs[iqs * 4    ] = values.x;
-    buf_b[buf_ib].qs[iqs * 4 + 1] = values.y;
-    buf_b[buf_ib].qs[iqs * 4 + 2] = values.z;
-    buf_b[buf_ib].qs[iqs * 4 + 3] = values.w;
 }
 
 void block_b_to_registers(const uint ib) {

ggml/src/ggml-vulkan/vulkan-shaders/quantize_q8_1.comp

@@ -61,7 +61,7 @@ void quantize() {
 
     const uint a_idx = ib * 8 + iqs;
 
-    vec4 vals = a_idx < p.ne ? data_a[a_idx] : vec4(0.0f);
+    vec4 vals = a_idx < p.ne / 4 ? data_a[a_idx] : vec4(0.0f);
     const vec4 abs_vals = abs(vals);
 
     // Find absolute max for each block

ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp

@@ -3,6 +3,32 @@
 #include "generic_binary_head.glsl"
 #include "types.glsl"
 
+#if RMS_NORM_ROPE_FUSION
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+
+// data is passed from rms_norm -> rope through shared memory.
+// rms_norm calls this data_d, rope calls this rope_data_a.
+// Binding 2 is not used
+shared FLOAT_TYPE rope_data_a[1024];
+#define data_d rope_data_a
+
+layout (binding = 3) readonly buffer R_Y {int rope_data_pos[];};
+layout (binding = 4) readonly buffer R_Z {float rope_data_ff[];};
+layout (binding = 5) writeonly buffer R_D {ROPE_D_TYPE rope_data_d[];};
+layout (binding = 6) readonly buffer R_I {uvec2 rope_data_i[];}; // indices for set_rows
+
+#include "rope_params.glsl"
+#include "rope_funcs.glsl"
+
+#define GGML_ROPE_TYPE_NORMAL 0
+#define GGML_ROPE_TYPE_NEOX   2
+#define GGML_ROPE_TYPE_MROPE  8
+#define GGML_ROPE_TYPE_VISION 24
+
+#endif
+
 #extension GL_EXT_control_flow_attributes : enable
 #define BLOCK_SIZE 512
 
@@ -28,8 +54,12 @@ void rms_norm(uint num_iters) {
 
     uint32_t a_offset = samp*stride_sample + channel*stride_channel + row*stride_row + get_aoffset();
     uint32_t b_offset = src1_idx(0, row, channel, samp) + get_boffset();
+#if RMS_NORM_ROPE_FUSION
+    // Per-row offset in shared memory
+    uint32_t d_offset = 0;
+#else
     uint32_t d_offset = ((samp*nchannels + channel)*nrows + row)*ncols + get_doffset();
-
+#endif
     FLOAT_TYPE sum = FLOAT_TYPE(0.0f); // partial sum for thread in warp
 
     [[unroll]] for (uint col = tid, idx = 0; idx < num_iters; col += BLOCK_SIZE, ++idx) {
@@ -79,6 +109,18 @@ void rms_norm(uint num_iters) {
             data_d[d_offset + col] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col]));
         }
     }
+#if RMS_NORM_ROPE_FUSION
+    barrier();
+    rope_params rp = p.rope;
+    uint rope_row = (samp*nchannels + channel)*nrows + row;
+    for (uint t = 2*tid; t < ncols; t += 2*BLOCK_SIZE) {
+        if (rp.rope_mode == GGML_ROPE_TYPE_NEOX) {
+            rope_neox(t, rope_row, rp);
+        } else if (rp.rope_mode == GGML_ROPE_TYPE_NORMAL) {
+            rope_norm(t, rope_row, rp);
+        }
+    }
+#endif
 }
 
 void main() {

ggml/src/ggml-vulkan/vulkan-shaders/rope_funcs.glsl

@@ -0,0 +1,227 @@
+
+float rope_yarn_ramp(const float low, const float high, const uint i0) {
+    const float y = (i0 / 2 - low) / max(0.001f, high - low);
+    return 1.0f - min(1.0f, max(0.0f, y));
+}
+
+uint rope_a_coord(const uint i0, const uint i01, const uint i02, rope_params p) {
+#if RMS_NORM_ROPE_FUSION
+    // Per-row offset in shared memory
+    const uint ix = i0;
+#else
+    const uint ix = i02*p.nb02 + i01*p.nb01 + i0;
+#endif
+    return ix;
+}
+
+void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out float sin_theta, rope_params p) {
+    float mscale = p.attn_factor;
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = p.freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (p.ext_factor != 0.0f) {
+        float ramp_mix = rope_yarn_ramp(p.corr_dims[0], p.corr_dims[1], i0) * p.ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * log(1.0f / p.freq_scale);
+    }
+    // Backprogagation uses inverted rotation
+    if (p.is_back != 0) {
+        theta = -theta;
+    }
+    cos_theta = cos(theta) * mscale;
+    sin_theta = sin(theta) * mscale;
+}
+
+void rope_norm(const uint i0, const uint i1, rope_params p) {
+    uint ne0 = p.ncols;
+    uint ne1 = p.p_delta_rows;
+
+    if (i0 >= ne0) {
+        return;
+    }
+
+    // i1 is actually i2*nb2+i1, but the rows are contiguous
+    const uint i01 = i1 % ne1;
+    const uint i02 = i1 / ne1;
+
+    uint idst = i1*ne0 + i0;
+    const uint ix = rope_a_coord(i0, i01, i02, p);
+
+    // Fusion optimization: ROPE + VIEW + SET_ROWS..
+    // The rope output is viewed as a 1D tensor and offset based on a row index in data_i.
+    if (p.set_rows_stride != 0) {
+        idst = i01*ne0 + i0;
+        idst += rope_data_i[i02].x * p.set_rows_stride;
+    }
+
+    if (i0 >= p.n_dims) {
+        rope_data_d[idst + 0] = ROPE_D_TYPE(rope_data_a[ix + 0]);
+        rope_data_d[idst + 1] = ROPE_D_TYPE(rope_data_a[ix + 1]);
+
+        return;
+    }
+
+    const float theta_base = rope_data_pos[i02] * pow(p.theta_scale, i0/2.0f);
+
+    const float freq_factor = p.has_ff != 0 ? rope_data_ff[i0/2] : 1.0f;
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta, p);
+
+    const float x0 = float(rope_data_a[ix + 0]);
+    const float x1 = float(rope_data_a[ix + 1]);
+
+    rope_data_d[idst + 0] = ROPE_D_TYPE(x0*cos_theta - x1*sin_theta);
+    rope_data_d[idst + 1] = ROPE_D_TYPE(x0*sin_theta + x1*cos_theta);
+}
+
+void rope_neox(const uint i0, const uint i1, rope_params p) {
+    uint ne0 = p.ncols;
+    uint ne1 = p.p_delta_rows;
+
+    if (i0 >= ne0) {
+        return;
+    }
+
+    const uint i01 = i1 % ne1;
+    const uint i02 = i1 / ne1;
+
+    uint idst = i1*ne0 + i0/2;
+    const uint ix = rope_a_coord(i0/2, i01, i02, p);
+
+    // Fusion optimization: ROPE + VIEW + SET_ROWS..
+    // The rope output is viewed as a 1D tensor and offset based on a row index in rope_data_i.
+    if (p.set_rows_stride != 0) {
+        idst = i01*ne0 + i0/2;
+        idst += rope_data_i[i02].x * p.set_rows_stride;
+    }
+
+    if (i0 >= p.n_dims) {
+        rope_data_d[idst + i0/2 + 0] = ROPE_D_TYPE(rope_data_a[ix + i0/2 + 0]);
+        rope_data_d[idst + i0/2 + 1] = ROPE_D_TYPE(rope_data_a[ix + i0/2 + 1]);
+
+        return;
+    }
+
+    const float theta_base = rope_data_pos[i02] * pow(p.theta_scale, i0/2.0f);
+
+    const float freq_factor = p.has_ff != 0 ? rope_data_ff[i0/2] : 1.0f;
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta, p);
+
+    const float x0 = float(rope_data_a[ix + 0]);
+    const float x1 = float(rope_data_a[ix + p.n_dims/2]);
+
+    rope_data_d[idst + 0]          = ROPE_D_TYPE(x0*cos_theta - x1*sin_theta);
+    rope_data_d[idst + p.n_dims/2] = ROPE_D_TYPE(x0*sin_theta + x1*cos_theta);
+}
+
+
+void rope_multi(const uint i0, const uint i1, rope_params p) {
+    uint ne0 = p.ncols;
+    uint ne1 = p.p_delta_rows;
+    uint ne2 = p.ne02;
+
+    if (i0 >= ne0) {
+        return;
+    }
+
+    const uint i01 = i1 % ne1;
+    const uint i02 = i1 / ne1;
+
+    const uint idst = i1*ne0 + i0/2;
+    const uint ix = rope_a_coord(i0/2, i01, i02, p);
+
+    if (i0 >= p.n_dims) {
+        rope_data_d[idst + i0/2 + 0] = ROPE_D_TYPE(rope_data_a[ix + i0/2 + 0]);
+        rope_data_d[idst + i0/2 + 1] = ROPE_D_TYPE(rope_data_a[ix + i0/2 + 1]);
+
+        return;
+    }
+
+    const int sect_dims = p.sections[0] + p.sections[1] + p.sections[2] + p.sections[3];
+    const int sec_w = p.sections[1] + p.sections[0];
+    const uint sector = (i0 / 2) % sect_dims;
+
+    float theta_base = 0.0;
+    if (p.is_imrope != 0) {
+        if (sector % 3 == 1 && sector < 3 * p.sections[1]) {
+            theta_base = rope_data_pos[i02 + ne2 * 1]*pow(p.theta_scale, i0/2.0f);
+        } else if (sector % 3 == 2 && sector < 3 * p.sections[2]) {
+            theta_base = rope_data_pos[i02 + ne2 * 2]*pow(p.theta_scale, i0/2.0f);
+        } else if (sector % 3 == 0 && sector < 3 * p.sections[0]) {
+            theta_base = rope_data_pos[i02]*pow(p.theta_scale, i0/2.0f);
+        } else {
+            theta_base = rope_data_pos[i02 + ne2 * 3]*pow(p.theta_scale, i0/2.0f);
+        }
+    } else {
+        if (sector < p.sections[0]) {
+            theta_base = rope_data_pos[i02]*pow(p.theta_scale, i0/2.0f);
+        }
+        else if (sector >= p.sections[0] && sector < sec_w) {
+            theta_base = rope_data_pos[i02 + ne2 * 1]*pow(p.theta_scale, i0/2.0f);
+        }
+        else if (sector >= sec_w && sector < sec_w + p.sections[2]) {
+            theta_base = rope_data_pos[i02 + ne2 * 2]*pow(p.theta_scale, i0/2.0f);
+        }
+        else if (sector >= sec_w + p.sections[2]) {
+            theta_base = rope_data_pos[i02 + ne2 * 3]*pow(p.theta_scale, i0/2.0f);
+        }
+    }
+
+    const float freq_factor = p.has_ff != 0 ? rope_data_ff[i0/2] : 1.0f;
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta, p);
+
+    const float x0 = float(rope_data_a[ix + 0]);
+    const float x1 = float(rope_data_a[ix + p.n_dims/2]);
+
+    rope_data_d[idst + 0]          = ROPE_D_TYPE(x0*cos_theta - x1*sin_theta);
+    rope_data_d[idst + p.n_dims/2] = ROPE_D_TYPE(x0*sin_theta + x1*cos_theta);
+}
+
+void rope_vision(const uint i0, const uint i1, rope_params p) {
+    uint ne0 = p.ncols;
+    uint ne1 = p.p_delta_rows;
+    uint ne2 = p.ne02;
+
+    if (i0 >= ne0) {
+        return;
+    }
+
+    const uint i01 = i1 % ne1;
+    const uint i02 = i1 / ne1;
+
+    const uint idst = i1*ne0 + i0/2;
+    const uint ix = rope_a_coord(i0/2, i01, i02, p);
+
+    const int sect_dims = p.sections[0] + p.sections[1];
+    const int sec_w = p.sections[1] + p.sections[0];
+    const uint sector = (i0 / 2) % sect_dims;
+
+    float theta_base = 0.0;
+    if (sector < p.sections[0]) {
+        const uint p0 = sector;
+        theta_base = rope_data_pos[i02]*pow(p.theta_scale, p0);
+    }
+    else if (sector >= p.sections[0] && sector < sec_w) {
+        const uint p0 = sector - p.sections[0];
+        theta_base = rope_data_pos[i02 + ne2]*pow(p.theta_scale, p0);
+    }
+
+    const float freq_factor = p.has_ff != 0 ? rope_data_ff[i0/2] : 1.0f;
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta, p);
+
+    const float x0 = float(rope_data_a[ix + 0]);
+    const float x1 = float(rope_data_a[ix + p.n_dims]);
+
+    rope_data_d[idst + 0]        = ROPE_D_TYPE(x0*cos_theta - x1*sin_theta);
+    rope_data_d[idst + p.n_dims] = ROPE_D_TYPE(x0*sin_theta + x1*cos_theta);
+}
+

ggml/src/ggml-vulkan/vulkan-shaders/rope_head.glsl

@@ -3,56 +3,18 @@
 #extension GL_EXT_shader_16bit_storage : require
 
 #include "rte.glsl"
+#include "rope_params.glsl"
 
 layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in;
 
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer Y {int data_pos[];};
-layout (binding = 2) readonly buffer Z {float data_ff[];};
-layout (binding = 3) writeonly buffer D {D_TYPE data_d[];};
-layout (binding = 4) readonly buffer I {uvec2 data_i[];}; // indices for set_rows
+layout (binding = 0) readonly buffer X {A_TYPE rope_data_a[];};
+layout (binding = 1) readonly buffer Y {int rope_data_pos[];};
+layout (binding = 2) readonly buffer Z {float rope_data_ff[];};
+layout (binding = 3) writeonly buffer D {ROPE_D_TYPE rope_data_d[];};
+layout (binding = 4) readonly buffer I {uvec2 rope_data_i[];}; // indices for set_rows
+
 
 layout (push_constant) uniform parameter {
-    uint ncols;
-    uint n_dims;
-    float freq_scale;
-    uint p_delta_rows;
-    float freq_base;
-    float ext_factor;
-    float attn_factor;
-    float corr_dims[2];
-    float theta_scale;
-    uint has_ff;
-    uint ne02;
-    uint s1;
-    uint s2;
-    int sections[4];
-    uint is_imrope;
-    uint is_back;
-    uint set_rows_stride;
-} p;
+    rope_params pc;
+};
 
-float rope_yarn_ramp(const float low, const float high, const uint i0) {
-    const float y = (i0 / 2 - low) / max(0.001f, high - low);
-    return 1.0f - min(1.0f, max(0.0f, y));
-}
-
-void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out float sin_theta) {
-    float mscale = p.attn_factor;
-    // Get n-d rotational scaling corrected for extrapolation
-    float theta_interp = p.freq_scale * theta_extrap;
-    float theta = theta_interp;
-    if (p.ext_factor != 0.0f) {
-        float ramp_mix = rope_yarn_ramp(p.corr_dims[0], p.corr_dims[1], i0) * p.ext_factor;
-        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
-
-        // Get n-d magnitude scaling corrected for interpolation
-        mscale *= 1.0f + 0.1f * log(1.0f / p.freq_scale);
-    }
-    // Backprogagation uses inverted rotation
-    if (p.is_back != 0) {
-        theta = -theta;
-    }
-    cos_theta = cos(theta) * mscale;
-    sin_theta = sin(theta) * mscale;
-}

ggml/src/ggml-vulkan/vulkan-shaders/rope_multi.comp

@@ -1,70 +1,11 @@
 #version 450
 
 #include "rope_head.glsl"
+#include "rope_funcs.glsl"
 
 void main() {
     const uint i0 = 2*gl_GlobalInvocationID.y;
-    uint ne0 = p.ncols;
-    uint ne1 = p.p_delta_rows;
-    uint ne2 = p.ne02;
-
-    if (i0 >= ne0) {
-        return;
-    }
-
-    const uint row_dst = gl_GlobalInvocationID.x;
-
-    const uint row_x     = row_dst % ne1;
-    const uint channel_x = row_dst / ne1;
-
-    const uint idst = row_dst*ne0 + i0/2;
-    const uint ix   = channel_x*p.s2 + row_x*p.s1 + i0/2;
-
-    if (i0 >= p.n_dims) {
-        data_d[idst + i0/2 + 0] = data_a[ix + i0/2 + 0];
-        data_d[idst + i0/2 + 1] = data_a[ix + i0/2 + 1];
-
-        return;
-    }
-
-    const int sect_dims = p.sections[0] + p.sections[1] + p.sections[2] + p.sections[3];
-    const int sec_w = p.sections[1] + p.sections[0];
-    const uint sector = (i0 / 2) % sect_dims;
-
-    float theta_base = 0.0;
-    if (p.is_imrope != 0) {
-        if (sector % 3 == 1 && sector < 3 * p.sections[1]) {
-            theta_base = data_pos[channel_x + ne2 * 1]*pow(p.theta_scale, i0/2.0f);
-        } else if (sector % 3 == 2 && sector < 3 * p.sections[2]) {
-            theta_base = data_pos[channel_x + ne2 * 2]*pow(p.theta_scale, i0/2.0f);
-        } else if (sector % 3 == 0 && sector < 3 * p.sections[0]) {
-            theta_base = data_pos[channel_x]*pow(p.theta_scale, i0/2.0f);
-        } else {
-            theta_base = data_pos[channel_x + ne2 * 3]*pow(p.theta_scale, i0/2.0f);
-        }
-    } else {
-        if (sector < p.sections[0]) {
-            theta_base = data_pos[channel_x]*pow(p.theta_scale, i0/2.0f);
-        }
-        else if (sector >= p.sections[0] && sector < sec_w) {
-            theta_base = data_pos[channel_x + ne2 * 1]*pow(p.theta_scale, i0/2.0f);
-        }
-        else if (sector >= sec_w && sector < sec_w + p.sections[2]) {
-            theta_base = data_pos[channel_x + ne2 * 2]*pow(p.theta_scale, i0/2.0f);
-        }
-        else if (sector >= sec_w + p.sections[2]) {
-            theta_base = data_pos[channel_x + ne2 * 3]*pow(p.theta_scale, i0/2.0f);
-        }
-    }
-
-    const float freq_factor = p.has_ff != 0 ? data_ff[i0/2] : 1.0f;
-
-    float cos_theta, sin_theta;
-    rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta);
-
-    const float x0 = float(data_a[ix + 0]);
-    const float x1 = float(data_a[ix + p.n_dims/2]);
-
-    data_d[idst + 0]          = D_TYPE(x0*cos_theta - x1*sin_theta);
-    data_d[idst + p.n_dims/2] = D_TYPE(x0*sin_theta + x1*cos_theta);
+    // i1 is actually i2*nb2+i1, but the rows are contiguous
+    const uint i1 = gl_GlobalInvocationID.x;
+    rope_multi(i0, i1, pc);
 }

ggml/src/ggml-vulkan/vulkan-shaders/rope_neox.comp

@@ -1,48 +1,11 @@
 #version 450
 
 #include "rope_head.glsl"
+#include "rope_funcs.glsl"
 
 void main() {
     const uint i0 = 2*gl_GlobalInvocationID.y;
-    uint ne0 = p.ncols;
-    uint ne1 = p.p_delta_rows;
-
-    if (i0 >= ne0) {
-        return;
-    }
-
-    const uint row_dst = gl_GlobalInvocationID.x;
-
-    const uint row_x     = row_dst % ne1;
-    const uint channel_x = row_dst / ne1;
-
-    uint idst = row_dst*ne0 + i0/2;
-    const uint ix   = channel_x*p.s2 + row_x*p.s1 + i0/2;
-
-    // Fusion optimization: ROPE + VIEW + SET_ROWS..
-    // The rope output is viewed as a 1D tensor and offset based on a row index in data_i.
-    if (p.set_rows_stride != 0) {
-        idst = row_x*ne0 + i0/2;
-        idst += data_i[channel_x].x * p.set_rows_stride;
-    }
-
-    if (i0 >= p.n_dims) {
-        data_d[idst + i0/2 + 0] = D_TYPE(data_a[ix + i0/2 + 0]);
-        data_d[idst + i0/2 + 1] = D_TYPE(data_a[ix + i0/2 + 1]);
-
-        return;
-    }
-
-    const float theta_base = data_pos[channel_x] * pow(p.theta_scale, i0/2.0f);
-
-    const float freq_factor = p.has_ff != 0 ? data_ff[i0/2] : 1.0f;
-
-    float cos_theta, sin_theta;
-    rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta);
-
-    const float x0 = float(data_a[ix + 0]);
-    const float x1 = float(data_a[ix + p.n_dims/2]);
-
-    data_d[idst + 0]          = D_TYPE(x0*cos_theta - x1*sin_theta);
-    data_d[idst + p.n_dims/2] = D_TYPE(x0*sin_theta + x1*cos_theta);
+    // i1 is actually i2*nb2+i1, but the rows are contiguous
+    const uint i1 = gl_GlobalInvocationID.x;
+    rope_neox(i0, i1, pc);
 }

ggml/src/ggml-vulkan/vulkan-shaders/rope_norm.comp

@@ -1,48 +1,11 @@
 #version 450
 
 #include "rope_head.glsl"
+#include "rope_funcs.glsl"
 
 void main() {
     const uint i0 = 2*gl_GlobalInvocationID.y;
-    uint ne0 = p.ncols;
-    uint ne1 = p.p_delta_rows;
-
-    if (i0 >= ne0) {
-        return;
-    }
-
-    const uint row_dst = gl_GlobalInvocationID.x;
-
-    const uint row_x     = row_dst % ne1;
-    const uint channel_x = row_dst / ne1;
-
-    uint idst = row_dst*ne0 + i0;
-    const uint ix   = channel_x*p.s2 + row_x*p.s1 + i0;
-
-    // Fusion optimization: ROPE + VIEW + SET_ROWS..
-    // The rope output is viewed as a 1D tensor and offset based on a row index in data_i.
-    if (p.set_rows_stride != 0) {
-        idst = row_x*ne0 + i0;
-        idst += data_i[channel_x].x * p.set_rows_stride;
-    }
-
-    if (i0 >= p.n_dims) {
-        data_d[idst + 0] = D_TYPE(data_a[ix + 0]);
-        data_d[idst + 1] = D_TYPE(data_a[ix + 1]);
-
-        return;
-    }
-
-    const float theta_base = data_pos[channel_x] * pow(p.theta_scale, i0/2.0f);
-
-    const float freq_factor = p.has_ff != 0 ? data_ff[i0/2] : 1.0f;
-
-    float cos_theta, sin_theta;
-    rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta);
-
-    const float x0 = float(data_a[ix + 0]);
-    const float x1 = float(data_a[ix + 1]);
-
-    data_d[idst + 0] = D_TYPE(x0*cos_theta - x1*sin_theta);
-    data_d[idst + 1] = D_TYPE(x0*sin_theta + x1*cos_theta);
+    // i1 is actually i2*nb2+i1, but the rows are contiguous
+    const uint i1 = gl_GlobalInvocationID.x;
+    rope_norm(i0, i1, pc);
 }

ggml/src/ggml-vulkan/vulkan-shaders/rope_params.glsl

@@ -0,0 +1,27 @@
+#if !defined(GGML_ROPE_PARAMS)
+#define GGML_ROPE_PARAMS
+
+#include "rte.glsl"
+
+struct rope_params {
+    uint rope_mode;
+    uint ncols;
+    uint n_dims;
+    float freq_scale;
+    uint p_delta_rows;
+    float freq_base;
+    float ext_factor;
+    float attn_factor;
+    float corr_dims[2];
+    float theta_scale;
+    uint has_ff;
+    uint ne02;
+    uint nb01;
+    uint nb02;
+    int sections[4];
+    uint is_imrope;
+    uint is_back;
+    uint set_rows_stride;
+};
+
+#endif // !defined(GGML_ROPE_PARAMS)

ggml/src/ggml-vulkan/vulkan-shaders/rope_vision.comp

@@ -1,47 +1,11 @@
 #version 450
 
 #include "rope_head.glsl"
+#include "rope_funcs.glsl"
 
 void main() {
     const uint i0 = 2*gl_GlobalInvocationID.y;
-    uint ne0 = p.ncols;
-    uint ne1 = p.p_delta_rows;
-    uint ne2 = p.ne02;
-
-    if (i0 >= ne0) {
-        return;
-    }
-
-    const uint row_dst = gl_GlobalInvocationID.x;
-
-    const uint row_x     = row_dst % ne1;
-    const uint channel_x = row_dst / ne1;
-
-    const uint idst = row_dst*ne0 + i0/2;
-    const uint ix   = channel_x*p.s2 + row_x*p.s1 + i0/2;
-
-    const int sect_dims = p.sections[0] + p.sections[1];
-    const int sec_w = p.sections[1] + p.sections[0];
-    const uint sector = (i0 / 2) % sect_dims;
-
-    float theta_base = 0.0;
-    if (sector < p.sections[0]) {
-        const uint p0 = sector;
-        theta_base = data_pos[channel_x]*pow(p.theta_scale, p0);
-    }
-    else if (sector >= p.sections[0] && sector < sec_w) {
-        const uint p0 = sector - p.sections[0];
-        theta_base = data_pos[channel_x + ne2]*pow(p.theta_scale, p0);
-    }
-
-    const float freq_factor = p.has_ff != 0 ? data_ff[i0/2] : 1.0f;
-
-    float cos_theta, sin_theta;
-    rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta);
-
-    const float x0 = float(data_a[ix + 0]);
-    const float x1 = float(data_a[ix + p.n_dims]);
-
-    data_d[idst + 0]        = D_TYPE(x0*cos_theta - x1*sin_theta);
-    data_d[idst + p.n_dims] = D_TYPE(x0*sin_theta + x1*cos_theta);
+    // i1 is actually i2*nb2+i1, but the rows are contiguous
+    const uint i1 = gl_GlobalInvocationID.x;
+    rope_vision(i0, i1, pc);
 }

ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp

@@ -20,6 +20,7 @@ layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
 // from ggml.h: enum ggml_scale_mode, enum ggml_scale_flag
 #define NEAREST  0
 #define BILINEAR 1
+#define BICUBIC  2
 
 layout (constant_id = 0) const uint scale_mode = 0;
 
@@ -61,6 +62,39 @@ float interpolate_bilinear(uint i10, uint i11, uint i12, uint i13) {
     return fetch_bilinear(c0, c1, d, i12, i13);
 }
 
+// Bicubic interpolation with alpha = -0.75
+// https://en.wikipedia.org/wiki/Bicubic_interpolation#Bicubic_convolution_algorithm
+const vec4 bcoeffs1 = vec4( 1.25, -2.25,  0.0, 1.0);
+const vec4 bcoeffs2 = vec4(-0.75,  3.75, -6.0, 3.0);
+vec4 powers(float x) { return vec4(x*x*x, x*x, x, 1); }
+
+float bicubic(float p0, float p1, float p2, float p3, float x) {
+    return p0 * dot(bcoeffs2, powers(x + 1)) +
+           p1 * dot(bcoeffs1, powers(x    )) +
+           p2 * dot(bcoeffs1, powers(1 - x)) +
+           p3 * dot(bcoeffs2, powers(2 - x));
+}
+
+#define FETCH(a,b) data_a[base + clamp(i.x+(a), 0, res.x) * p.nb00 + clamp(i.y+(b), 0, res.y) * p.nb01]
+
+float interpolate_bicubic(uint i10, uint i11, uint i12, uint i13) {
+    const ivec2 res = ivec2(p.ne00 - 1, p.ne01 - 1);
+
+    const vec2 coord = (vec2(i10, i11) + p.pixel_offset) / vec2(p.sf0, p.sf1) - p.pixel_offset;
+    const vec2 d = fract(coord);
+    const ivec2 i = ivec2(floor(coord));
+
+    const uint i02 = uint(i12 / p.sf2);
+    const uint i03 = uint(i13 / p.sf3);
+    const uint base = p.a_offset + i03 * p.nb03 + i02 * p.nb02;
+
+    return bicubic(
+        bicubic(FETCH(-1,-1), FETCH(0,-1), FETCH(1,-1), FETCH(2,-1), d.x),
+        bicubic(FETCH(-1, 0), FETCH(0, 0), FETCH(1, 0), FETCH(2, 0), d.x),
+        bicubic(FETCH(-1, 1), FETCH(0, 1), FETCH(1, 1), FETCH(2, 1), d.x),
+        bicubic(FETCH(-1, 2), FETCH(0, 2), FETCH(1, 2), FETCH(2, 2), d.x), d.y);
+}
+
 void main() {
     const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
 
@@ -81,6 +115,9 @@ void main() {
         case BILINEAR:
             result = interpolate_bilinear(i10, i11, i12, i13);
             break;
+        case BICUBIC:
+            result = interpolate_bicubic(i10, i11, i12, i13);
+            break;
     }
 
     data_d[p.d_offset + idx] = D_TYPE(result);

ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp

@@ -695,6 +695,8 @@ void process_shaders() {
     string_to_spv("group_norm_f32", "group_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
     string_to_spv("rms_norm_f32", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
     string_to_spv("rms_norm_partials_f32", "rms_norm_partials.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
+    string_to_spv("rms_norm_mul_rope_f32_f32", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"ROPE_D_TYPE", "float"}, {"RMS_NORM_ROPE_FUSION", "1"}}));
+    string_to_spv("rms_norm_mul_rope_f32_f16_rte", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"ROPE_D_TYPE", "float16_t"}, {"RMS_NORM_ROPE_FUSION", "1"}, {"RTE16", "1"}}));
     string_to_spv("rms_norm_back_f32", "rms_norm_back.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
     string_to_spv("l2_norm_f32", "l2_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
 
@@ -840,25 +842,25 @@ void process_shaders() {
     string_to_spv("soft_max_f32_f16", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
     string_to_spv("soft_max_back_f32", "soft_max_back.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
 
-    string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
-    string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
-    string_to_spv("rope_norm_f16_rte", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"RTE16", "1"}});
-    string_to_spv("rope_norm_f32_f16", "rope_norm.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}});
-    string_to_spv("rope_norm_f32_f16_rte", "rope_norm.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"RTE16", "1"}});
+    string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}});
+    string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}});
+    string_to_spv("rope_norm_f16_rte", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}});
+    string_to_spv("rope_norm_f32_f16", "rope_norm.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float16_t"}});
+    string_to_spv("rope_norm_f32_f16_rte", "rope_norm.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}});
 
-    string_to_spv("rope_neox_f32", "rope_neox.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
-    string_to_spv("rope_neox_f16", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
-    string_to_spv("rope_neox_f16_rte", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"RTE16", "1"}});
-    string_to_spv("rope_neox_f32_f16", "rope_neox.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}});
-    string_to_spv("rope_neox_f32_f16_rte", "rope_neox.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"RTE16", "1"}});
+    string_to_spv("rope_neox_f32", "rope_neox.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}});
+    string_to_spv("rope_neox_f16", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}});
+    string_to_spv("rope_neox_f16_rte", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}});
+    string_to_spv("rope_neox_f32_f16", "rope_neox.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float16_t"}});
+    string_to_spv("rope_neox_f32_f16_rte", "rope_neox.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}});
 
-    string_to_spv("rope_multi_f32", "rope_multi.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
-    string_to_spv("rope_multi_f16", "rope_multi.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
-    string_to_spv("rope_multi_f16_rte", "rope_multi.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"RTE16", "1"}});
+    string_to_spv("rope_multi_f32", "rope_multi.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}});
+    string_to_spv("rope_multi_f16", "rope_multi.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}});
+    string_to_spv("rope_multi_f16_rte", "rope_multi.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}});
 
-    string_to_spv("rope_vision_f32", "rope_vision.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
-    string_to_spv("rope_vision_f16", "rope_vision.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
-    string_to_spv("rope_vision_f16_rte", "rope_vision.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"RTE16", "1"}});
+    string_to_spv("rope_vision_f32", "rope_vision.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}});
+    string_to_spv("rope_vision_f16", "rope_vision.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}});
+    string_to_spv("rope_vision_f16_rte", "rope_vision.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}});
 
     string_to_spv("argsort_f32", "argsort.comp", {{"A_TYPE", "float"}});
 

ggml/src/ggml-webgpu/ggml-webgpu.cpp

@@ -15,6 +15,7 @@
 #include <condition_variable>
 #include <cstring>
 #include <iostream>
+#include <map>
 #include <mutex>
 #include <optional>
 #include <string>
@@ -73,6 +74,30 @@
 // For operations which process a row in parallel, this seems like a reasonable default
 #define WEBGPU_ROW_SPLIT_WG_SIZE 64
 
+// Matrix multiplication parameters
+
+// Register tiling parameters
+#define WEBGPU_MUL_MAT_TILE_M    8
+#define WEBGPU_MUL_MAT_TILE_N    8
+#define WEBGPU_MUL_MAT_WG_SIZE_M 8
+#define WEBGPU_MUL_MAT_WG_SIZE_N 8
+#define WEBGPU_MUL_MAT_TILE_K    32
+
+// Subgroup matrix parameters
+// The number of subgroups in the M dimension
+#define WEBGPU_MUL_MAT_SUBGROUP_M        2
+// The number of subgroups in the N dimension
+#define WEBGPU_MUL_MAT_SUBGROUP_N        2
+// The number of subgroup matrices each subgroup accumulates over
+#define WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M 4
+#define WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N 2
+
+// Matrix-vector multiplication parameters
+#define WEBGPU_MUL_MAT_VEC_WG_SIZE        256
+// Must be multiple of 4 to work with vectorized paths, and must divide mul_mat_vec wg size
+#define WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG 64
+#define WEBGPU_MUL_MAT_VEC_TILE_K         256
+
 /* End Constants */
 
 // This is a "fake" base pointer, since WebGPU buffers do not have pointers to their locations.
@@ -236,6 +261,10 @@ struct webgpu_context_struct {
     wgpu::Queue    queue;
     wgpu::Limits   limits;
 
+    bool                       supports_subgroup_matrix = false;
+    uint32_t                   subgroup_size;
+    wgpu::SubgroupMatrixConfig subgroup_matrix_config;
+
     // Separate this out from limits since on some Metal systems, the limit returned by
     // querying the limits is higher than the actual allowed maximum.
     uint32_t max_wg_size_x;
@@ -247,6 +276,11 @@ struct webgpu_context_struct {
     webgpu_buf_pool set_rows_error_buf_pool;
 
     webgpu_pipeline memset_pipeline;
+
+    std::map<int, std::map<int, std::map<int, webgpu_pipeline>>> mul_mat_pipelines;  // src0_type, src1_type, vectorized
+    std::map<int, std::map<int, std::map<int, webgpu_pipeline>>>
+        mul_mat_vec_pipelines;                                                       // src0_type, src1_type, vectorized
+
     webgpu_pipeline mul_mat_pipeline[30][2];
     webgpu_pipeline set_rows_pipeline[1][2];  // dst->type, vectorized
     webgpu_pipeline get_rows_pipeline[30];
@@ -321,6 +355,25 @@ struct ggml_backend_webgpu_buffer_context {
 
 /* WebGPU object initializations */
 
+// Process a WGSL shader string, replacing tokens of the form {{KEY}} with
+// the corresponding values provided in `repls`.
+static std::string ggml_webgpu_process_shader_repls(const char *                               src,
+                                                    const std::map<std::string, std::string> & repls) {
+    if (!src) {
+        return std::string();
+    }
+    std::string s = src;
+    for (const auto & kv : repls) {
+        std::string token = "{{" + kv.first + "}}";
+        size_t      pos   = 0;
+        while ((pos = s.find(token, pos)) != std::string::npos) {
+            s.replace(pos, token.length(), kv.second);
+            pos += kv.second.length();
+        }
+    }
+    return s;
+}
+
 static void ggml_webgpu_create_pipeline(wgpu::Device &                           device,
                                         webgpu_pipeline &                        pipeline,
                                         const char *                             shader_code,
@@ -346,6 +399,30 @@ static void ggml_webgpu_create_pipeline(wgpu::Device &
     pipeline = { device.CreateComputePipeline(&pipeline_desc), label };
 }
 
+static webgpu_pipeline ggml_webgpu_create_pipeline2(wgpu::Device &                           device,
+                                                    const char *                             shader_code,
+                                                    const char *                             label,
+                                                    const std::vector<wgpu::ConstantEntry> & constants = {}) {
+    wgpu::ShaderSourceWGSL shader_source;
+    shader_source.code = shader_code;
+
+    wgpu::ShaderModuleDescriptor shader_desc;
+    shader_desc.nextInChain = &shader_source;
+
+    wgpu::ShaderModule shader_module = device.CreateShaderModule(&shader_desc);
+
+    wgpu::ComputePipelineDescriptor pipeline_desc;
+    pipeline_desc.label              = label;
+    pipeline_desc.compute.module     = shader_module;
+    pipeline_desc.compute.entryPoint = "main";   // Entry point in the WGSL code
+    pipeline_desc.layout             = nullptr;  // nullptr means auto layout
+    if (constants.size() > 0) {
+        pipeline_desc.compute.constants     = constants.data();
+        pipeline_desc.compute.constantCount = constants.size();
+    }
+    return { device.CreateComputePipeline(&pipeline_desc), label };
+}
+
 static void ggml_webgpu_create_buffer(wgpu::Device &    device,
                                       wgpu::Buffer &    buffer,
                                       size_t            size,
@@ -512,6 +589,7 @@ static webgpu_command ggml_backend_webgpu_build(webgpu_context &
                                                 std::vector<uint32_t>             params,
                                                 std::vector<wgpu::BindGroupEntry> bind_group_entries,
                                                 uint32_t                          wg_x,
+                                                uint32_t                          wg_y                = 1,
                                                 std::optional<webgpu_pool_bufs>   set_rows_error_bufs = std::nullopt) {
     webgpu_pool_bufs params_bufs = ctx->param_buf_pool.alloc_bufs();
 
@@ -557,7 +635,7 @@ static webgpu_command ggml_backend_webgpu_build(webgpu_context &
 #endif
     pass.SetPipeline(pipeline.pipeline);
     pass.SetBindGroup(0, bind_group);
-    pass.DispatchWorkgroups(wg_x, 1, 1);
+    pass.DispatchWorkgroups(wg_x, wg_y, 1);
     pass.End();
 
 #ifdef GGML_WEBGPU_GPU_PROFILE
@@ -779,7 +857,7 @@ static std::optional<webgpu_command> ggml_webgpu_set_rows(webgpu_context & ctx,
 
     uint32_t wg_x = (threads + max_wg_size - 1) / max_wg_size;
 
-    return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, error_bufs);
+    return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, 1, error_bufs);
 }
 
 static webgpu_command ggml_webgpu_get_rows(webgpu_context & ctx,
@@ -835,8 +913,8 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx,
         (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
         (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)),
         (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
-        (uint32_t) dst->ne[1],                                  // number of rows in result (M)
-        (uint32_t) dst->ne[0],                                  // number of columns in result (N)
+        (uint32_t) dst->ne[0],                                  // number of rows in result (M, transposed)
+        (uint32_t) dst->ne[1],                                  // number of columns in result (N)
         (uint32_t) src0->ne[0],                                 // number of columns in src0/src1 (K)
         (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)),  // stride (elements/blocks) of src0 in dimension 1
         (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)),  // stride (elements/blocks) of src1 in dimension 1
@@ -865,9 +943,67 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx,
          .size    = ggml_webgpu_tensor_binding_size(ctx, dst)  },
     };
 
+    webgpu_pipeline pipeline = ctx->mul_mat_pipeline[src0->type][src1->type];
+
     uint32_t wg_x =
         (dst->ne[0] * dst->ne[1] * dst->ne[2] * dst->ne[3] + WEBGPU_MUL_MAT_WG_SIZE - 1) / WEBGPU_MUL_MAT_WG_SIZE;
-    return ggml_backend_webgpu_build(ctx, ctx->mul_mat_pipeline[src0->type][src1->type], params, entries, wg_x);
+    uint32_t wg_y = 1;
+
+    bool use_fast = false;
+    switch (src1->type) {
+        case GGML_TYPE_F16:
+            use_fast = (src0->type == GGML_TYPE_F16);
+            break;
+        case GGML_TYPE_F32:
+            switch (src0->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_F16:
+                case GGML_TYPE_Q4_0:
+                    use_fast = true;
+                    break;
+                default:
+                    break;
+            }
+            break;
+        default:
+            break;
+    }
+
+    if (use_fast) {
+        int vectorized = src0->ne[0] % 4 == 0 && dst->ne[0] % 4 == 0 && dst->ne[1] % 4 == 0;
+        if (dst->ne[1] == 1) {
+            // We don't support vectorized mul_mat_vec for quantized types
+            vectorized       = vectorized && (src0->type < 2);
+            pipeline         = ctx->mul_mat_vec_pipelines[src0->type][src1->type][vectorized];
+            uint32_t batches = dst->ne[2] * dst->ne[3];
+            uint32_t output_groups =
+                (dst->ne[0] + WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG - 1) / WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG;
+            uint32_t total_wg = output_groups * batches;
+            wg_x              = total_wg % ctx->limits.maxComputeWorkgroupsPerDimension;
+            wg_y              = (total_wg + ctx->limits.maxComputeWorkgroupsPerDimension - 1) /
+                   ctx->limits.maxComputeWorkgroupsPerDimension;
+        } else {
+            pipeline = ctx->mul_mat_pipelines[src0->type][src1->type][vectorized];
+            uint32_t wg_m;
+            uint32_t wg_n;
+            if (ctx->supports_subgroup_matrix) {
+                // The total number of subgroups/workgroups needed per matrix.
+                uint32_t wg_m_sg_tile =
+                    WEBGPU_MUL_MAT_SUBGROUP_M * WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M * ctx->subgroup_matrix_config.M;
+                wg_m = (dst->ne[0] + wg_m_sg_tile - 1) / wg_m_sg_tile;
+                uint32_t wg_n_sg_tile =
+                    WEBGPU_MUL_MAT_SUBGROUP_N * WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N * ctx->subgroup_matrix_config.N;
+                wg_n = (dst->ne[1] + wg_n_sg_tile - 1) / wg_n_sg_tile;
+            } else {
+                uint32_t tile_m_s = WEBGPU_MUL_MAT_TILE_M * WEBGPU_MUL_MAT_WG_SIZE_M;
+                uint32_t tile_n_s = WEBGPU_MUL_MAT_TILE_N * WEBGPU_MUL_MAT_WG_SIZE_N;
+                wg_m              = (dst->ne[0] + tile_m_s - 1) / tile_m_s;
+                wg_n              = (dst->ne[1] + tile_n_s - 1) / tile_n_s;
+            }
+            wg_x = wg_m * wg_n * dst->ne[2] * dst->ne[3];
+        }
+    }
+    return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
 }
 
 static webgpu_command ggml_webgpu_binary_op(webgpu_context &  ctx,
@@ -1583,12 +1719,6 @@ static void ggml_webgpu_init_memset_pipeline(webgpu_context & webgpu_ctx) {
 }
 
 static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) {
-    ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_F32][GGML_TYPE_F32],
-                                wgsl_mul_mat_f32_f32, "mul_mat_f32_f32");
-    ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_F16][GGML_TYPE_F16],
-                                wgsl_mul_mat_f16_f16, "mul_mat_f16_f16");
-    ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_F16][GGML_TYPE_F32],
-                                wgsl_mul_mat_f16_f32, "mul_mat_f16_f32");
     ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q4_0][GGML_TYPE_F32],
                                 wgsl_mul_mat_q4_0_f32, "mul_mat_q4_0_f32");
     ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q4_1][GGML_TYPE_F32],
@@ -1627,6 +1757,136 @@ static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) {
                                 wgsl_mul_mat_iq4_nl_f32, "mul_mat_iq4_nl_f32");
     ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ4_XS][GGML_TYPE_F32],
                                 wgsl_mul_mat_iq4_xs_f32, "mul_mat_iq4_xs_f32");
+
+    if (webgpu_ctx->supports_subgroup_matrix) {
+        std::map<std::string, std::string> sg_matrix_repls;
+        sg_matrix_repls["WEBGPU_MAX_SUBGROUP_SIZE"] = std::to_string(webgpu_ctx->subgroup_size);
+        sg_matrix_repls["WEBGPU_TILE_K"]            = std::to_string(WEBGPU_MUL_MAT_TILE_K);
+        sg_matrix_repls["WEBGPU_SUBGROUP_M"]        = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_M);
+        sg_matrix_repls["WEBGPU_SUBGROUP_N"]        = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_N);
+        sg_matrix_repls["WEBGPU_SUBGROUP_MATRIX_M"] = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M);
+        sg_matrix_repls["WEBGPU_SUBGROUP_MATRIX_N"] = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N);
+        sg_matrix_repls["WEBGPU_SG_MAT_M_SIZE"]     = std::to_string(webgpu_ctx->subgroup_matrix_config.M);
+        sg_matrix_repls["WEBGPU_SG_MAT_N_SIZE"]     = std::to_string(webgpu_ctx->subgroup_matrix_config.N);
+        sg_matrix_repls["WEBGPU_SG_MAT_K_SIZE"]     = std::to_string(webgpu_ctx->subgroup_matrix_config.K);
+
+        std::string proc_mul_mat_subgroup_matrix_f32_f32 =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_f32_f32, sg_matrix_repls);
+        std::string proc_mul_mat_subgroup_matrix_f32_f32_vec =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_f32_f32_vec, sg_matrix_repls);
+        std::string proc_mul_mat_subgroup_matrix_f16_f32 =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_f16_f32, sg_matrix_repls);
+        std::string proc_mul_mat_subgroup_matrix_f16_f32_vec =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_f16_f32_vec, sg_matrix_repls);
+        std::string proc_mul_mat_subgroup_matrix_f16_f16 =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_f16_f16, sg_matrix_repls);
+        std::string proc_mul_mat_subgroup_matrix_f16_f16_vec =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_f16_f16_vec, sg_matrix_repls);
+        std::string proc_mul_mat_subgroup_matrix_q4_0_f32 =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_q4_0_f32, sg_matrix_repls);
+        std::string proc_mul_mat_subgroup_matrix_q4_0_f32_vec =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_q4_0_f32_vec, sg_matrix_repls);
+
+        webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2(
+            webgpu_ctx->device, proc_mul_mat_subgroup_matrix_f32_f32.c_str(), "mul_mat_subgroup_matrix_f32_f32");
+        webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] =
+            ggml_webgpu_create_pipeline2(webgpu_ctx->device, proc_mul_mat_subgroup_matrix_f32_f32_vec.c_str(),
+                                         "mul_mat_subgroup_matrix_f32_f32_vec");
+        webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2(
+            webgpu_ctx->device, proc_mul_mat_subgroup_matrix_f16_f32.c_str(), "mul_mat_subgroup_matrix_f16_f32");
+        webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] =
+            ggml_webgpu_create_pipeline2(webgpu_ctx->device, proc_mul_mat_subgroup_matrix_f16_f32_vec.c_str(),
+                                         "mul_mat_subgroup_matrix_f16_f32_vec");
+        webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline2(
+            webgpu_ctx->device, proc_mul_mat_subgroup_matrix_f16_f16.c_str(), "mul_mat_subgroup_matrix_f16_f16");
+        webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] =
+            ggml_webgpu_create_pipeline2(webgpu_ctx->device, proc_mul_mat_subgroup_matrix_f16_f16_vec.c_str(),
+                                         "mul_mat_subgroup_matrix_f16_f16_vec");
+        webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2(
+            webgpu_ctx->device, proc_mul_mat_subgroup_matrix_q4_0_f32.c_str(), "mul_mat_subgroup_matrix_q4_0_f32");
+        webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][1] =
+            ggml_webgpu_create_pipeline2(webgpu_ctx->device, proc_mul_mat_subgroup_matrix_q4_0_f32_vec.c_str(),
+                                         "mul_mat_subgroup_matrix_q4_0_f32_vec");
+    } else {
+        std::vector<wgpu::ConstantEntry> mul_mat_reg_tile_constants(3);
+        mul_mat_reg_tile_constants[0].key   = "TILE_K";
+        mul_mat_reg_tile_constants[0].value = WEBGPU_MUL_MAT_TILE_K;
+        mul_mat_reg_tile_constants[1].key   = "WORKGROUP_SIZE_M";
+        mul_mat_reg_tile_constants[1].value = WEBGPU_MUL_MAT_WG_SIZE_M;
+        mul_mat_reg_tile_constants[2].key   = "WORKGROUP_SIZE_N";
+        mul_mat_reg_tile_constants[2].value = WEBGPU_MUL_MAT_WG_SIZE_N;
+
+        std::map<std::string, std::string> reg_repls;
+        reg_repls["WEBGPU_TILE_M"] = std::to_string(WEBGPU_MUL_MAT_TILE_M);
+        reg_repls["WEBGPU_TILE_N"] = std::to_string(WEBGPU_MUL_MAT_TILE_N);
+
+        // Process each reg-tile shader with tile replacements.
+        // Keep the processed strings in-scope so .c_str() remains valid.
+        std::string proc_mul_mat_reg_tile_f32_f32 =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_reg_tile_f32_f32, reg_repls);
+        std::string proc_mul_mat_reg_tile_f32_f32_vec =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_reg_tile_f32_f32_vec, reg_repls);
+        std::string proc_mul_mat_reg_tile_f16_f32 =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_reg_tile_f16_f32, reg_repls);
+        std::string proc_mul_mat_reg_tile_f16_f32_vec =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_reg_tile_f16_f32_vec, reg_repls);
+        std::string proc_mul_mat_reg_tile_f16_f16 =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_reg_tile_f16_f16, reg_repls);
+        std::string proc_mul_mat_reg_tile_f16_f16_vec =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_reg_tile_f16_f16_vec, reg_repls);
+        std::string proc_mul_mat_reg_tile_q4_0_f32 =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_reg_tile_q4_0_f32, reg_repls);
+        std::string proc_mul_mat_reg_tile_q4_0_f32_vec =
+            ggml_webgpu_process_shader_repls(wgsl_mul_mat_reg_tile_q4_0_f32_vec, reg_repls);
+
+        webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] =
+            ggml_webgpu_create_pipeline2(webgpu_ctx->device, proc_mul_mat_reg_tile_f32_f32.c_str(),
+                                         "mul_mat_reg_tile_f32_f32", mul_mat_reg_tile_constants);
+        webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] =
+            ggml_webgpu_create_pipeline2(webgpu_ctx->device, proc_mul_mat_reg_tile_f32_f32_vec.c_str(),
+                                         "mul_mat_reg_tile_f32_f32_vec", mul_mat_reg_tile_constants);
+        webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] =
+            ggml_webgpu_create_pipeline2(webgpu_ctx->device, proc_mul_mat_reg_tile_f16_f32.c_str(),
+                                         "mul_mat_reg_tile_f16_f32", mul_mat_reg_tile_constants);
+        webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] =
+            ggml_webgpu_create_pipeline2(webgpu_ctx->device, proc_mul_mat_reg_tile_f16_f32_vec.c_str(),
+                                         "mul_mat_reg_tile_f16_f32_vec", mul_mat_reg_tile_constants);
+        webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] =
+            ggml_webgpu_create_pipeline2(webgpu_ctx->device, proc_mul_mat_reg_tile_f16_f16.c_str(),
+                                         "mul_mat_reg_tile_f16_f16", mul_mat_reg_tile_constants);
+        webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] =
+            ggml_webgpu_create_pipeline2(webgpu_ctx->device, proc_mul_mat_reg_tile_f16_f16_vec.c_str(),
+                                         "mul_mat_reg_tile_f16_f16_vec", mul_mat_reg_tile_constants);
+        webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] =
+            ggml_webgpu_create_pipeline2(webgpu_ctx->device, proc_mul_mat_reg_tile_q4_0_f32.c_str(),
+                                         "mul_mat_reg_tile_q4_0_f32", mul_mat_reg_tile_constants);
+        webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][1] =
+            ggml_webgpu_create_pipeline2(webgpu_ctx->device, proc_mul_mat_reg_tile_q4_0_f32_vec.c_str(),
+                                         "mul_mat_reg_tile_q4_0_f32_vec", mul_mat_reg_tile_constants);
+    }
+
+    std::vector<wgpu::ConstantEntry> mul_mat_vec_constants(3);
+    mul_mat_vec_constants[0].key   = "WORKGROUP_SIZE";
+    mul_mat_vec_constants[0].value = WEBGPU_MUL_MAT_VEC_WG_SIZE;
+    mul_mat_vec_constants[1].key   = "TILE_K";
+    mul_mat_vec_constants[1].value = WEBGPU_MUL_MAT_VEC_TILE_K;
+    mul_mat_vec_constants[2].key   = "OUTPUTS_PER_WG";
+    mul_mat_vec_constants[2].value = WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG;
+
+    webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2(
+        webgpu_ctx->device, wgsl_mul_mat_vec_f32_f32, "mul_mat_vec_f32_f32", mul_mat_vec_constants);
+    webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline2(
+        webgpu_ctx->device, wgsl_mul_mat_vec_f32_f32_vec, "mul_mat_vec_f32_f32_vec", mul_mat_vec_constants);
+    webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2(
+        webgpu_ctx->device, wgsl_mul_mat_vec_f16_f32, "mul_mat_vec_f16_f32", mul_mat_vec_constants);
+    webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline2(
+        webgpu_ctx->device, wgsl_mul_mat_vec_f16_f32_vec, "mul_mat_vec_f16_f32_vec", mul_mat_vec_constants);
+    webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline2(
+        webgpu_ctx->device, wgsl_mul_mat_vec_f16_f16, "mul_mat_vec_f16_f16", mul_mat_vec_constants);
+    webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline2(
+        webgpu_ctx->device, wgsl_mul_mat_vec_f16_f16_vec, "mul_mat_vec_f16_f16_vec", mul_mat_vec_constants);
+    webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2(
+        webgpu_ctx->device, wgsl_mul_mat_vec_q4_0_f32, "mul_mat_vec_q4_0_f32", mul_mat_vec_constants);
 }
 
 static void ggml_webgpu_init_set_rows_pipeline(webgpu_context & webgpu_ctx) {
@@ -2124,7 +2384,13 @@ static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t
 
     webgpu_context ctx = reg_ctx->webgpu_ctx;
 
-    wgpu::RequestAdapterOptions options = {};
+    // TODO: track need for these toggles: https://issues.chromium.org/issues/42251215
+    const char * const          adapterEnabledToggles[] = { "vulkan_enable_f16_on_nvidia", "use_vulkan_memory_model" };
+    wgpu::DawnTogglesDescriptor adapterTogglesDesc;
+    adapterTogglesDesc.enabledToggles     = adapterEnabledToggles;
+    adapterTogglesDesc.enabledToggleCount = 2;
+    wgpu::RequestAdapterOptions options   = {};
+    options.nextInChain                   = &adapterTogglesDesc;
     ctx->instance.WaitAny(ctx->instance.RequestAdapter(
                               &options, wgpu::CallbackMode::AllowSpontaneous,
                               [&ctx](wgpu::RequestAdapterStatus status, wgpu::Adapter adapter, const char * message) {
@@ -2140,12 +2406,46 @@ static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t
     ctx->adapter.GetLimits(&ctx->limits);
     ctx->max_wg_size_x = 288;  // default value
 
-    wgpu::AdapterInfo info{};
+    wgpu::AdapterInfo                            info{};
+    wgpu::AdapterPropertiesSubgroupMatrixConfigs subgroup_matrix_configs{};
+    if (ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) {
+        info.nextInChain = &subgroup_matrix_configs;
+    }
     ctx->adapter.GetInfo(&info);
 
+    wgpu::SupportedFeatures features;
+    ctx->adapter.GetFeatures(&features);
+    // we require f16 support
+    GGML_ASSERT(ctx->adapter.HasFeature(wgpu::FeatureName::ShaderF16));
+
+    // Only support square f16 matrices of size 8 or 16 for now
+    bool valid_subgroup_matrix_config = false;
+    if (ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) {
+        for (size_t i = 0; i < subgroup_matrix_configs.configCount; i++) {
+            const wgpu::SubgroupMatrixConfig config = subgroup_matrix_configs.configs[i];
+            if (config.M == config.N && config.N == config.K && (config.K == 8 || config.K == 16) &&
+                config.componentType == wgpu::SubgroupMatrixComponentType::F16 &&
+                config.resultComponentType == wgpu::SubgroupMatrixComponentType::F16) {
+                ctx->subgroup_matrix_config  = config;
+                valid_subgroup_matrix_config = true;
+                break;
+            }
+        }
+    }
+
+    // For subgroup matrix code to be the most efficient, we would like the subgroup size to be consistent and accurate.
+    // Unfortunately, that is not possible, so we use the maximum subgroup size reported by the adapter.
+    ctx->subgroup_size            = info.subgroupMaxSize;
+    ctx->supports_subgroup_matrix = valid_subgroup_matrix_config;
+
     // Initialize device
     std::vector<wgpu::FeatureName> required_features = { wgpu::FeatureName::ShaderF16,
                                                          wgpu::FeatureName::ImplicitDeviceSynchronization };
+    if (ctx->supports_subgroup_matrix) {
+        required_features.push_back(wgpu::FeatureName::Subgroups);
+        required_features.push_back(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix);
+    }
+
 #ifdef GGML_WEBGPU_GPU_PROFILE
     required_features.push_back(wgpu::FeatureName::TimestampQuery);
 #endif

ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py

@@ -72,9 +72,12 @@ def generate_variants(fname, input_dir, output_dir, outfile):
         except ValueError:
             decls_map = {}
 
-        with open(os.path.join(input_dir, "common_decls.tmpl"), "r", encoding="utf-8") as f:
-            common_decls = f.read()
-        decls_map.update(parse_decls(common_decls))
+        for fname in sorted(os.listdir(input_dir)):
+            if fname.endswith(".tmpl"):
+                tmpl_path = os.path.join(input_dir, fname)
+                with open(tmpl_path, "r", encoding="utf-8") as f_tmpl:
+                    decls = f_tmpl.read()
+                    decls_map.update(parse_decls(decls))
 
         shader_template = extract_block(text, "SHADER")
         for variant in variants:

ggml/src/ggml-webgpu/wgsl-shaders/mul_mat.tmpl.wgsl

@@ -864,8 +864,8 @@ struct MulMatParams {
     broadcast3: u32
 };
 
-@group(0) @binding(0) var<storage, read_write> src0: array<{{SRC0_TYPE}}>; // N rows, K columns
-@group(0) @binding(1) var<storage, read_write> src1: array<{{SRC1_TYPE}}>; // M rows, K columns (transposed)
+@group(0) @binding(0) var<storage, read_write> src0: array<{{SRC0_TYPE}}>; // M rows, K columns
+@group(0) @binding(1) var<storage, read_write> src1: array<{{SRC1_TYPE}}>; // K rows, N columns (transposed)
 @group(0) @binding(2) var<storage, read_write> dst: array<f32>; // M rows, N columns
 
 @group(0) @binding(3) var<uniform> params: MulMatParams;
@@ -891,8 +891,8 @@ fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {
 
     let dst2_rem = dst3_rem % dst2_stride;
 
-    let row = dst2_rem / params.n; // output row
-    let col = dst2_rem % params.n; // output column
+    let row = dst2_rem / params.m; // output row
+    let col = dst2_rem % params.m; // output column
 
     let src0_idx_base = params.offset_src0 + src03_idx * params.stride_03 + src02_idx * params.stride_02 + col * params.stride_01;
     let src1_idx_base = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12 + row * params.stride_11;
@@ -901,7 +901,7 @@ fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {
     for (var i: u32 = 0u; i < params.k/{{BLOCK_SIZE}}; i = i + 1u) {
         sum += multiply_add(src0_idx_base, src1_idx_base, i);
     }
-    dst[params.offset_dst + dst3_idx * dst3_stride + dst2_idx * dst2_stride + row * params.n + col] = sum;
+    dst[params.offset_dst + dst3_idx * dst3_stride + dst2_idx * dst2_stride + row * params.m + col] = sum;
 }
 
 #end(SHADER)

ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl

@@ -0,0 +1,97 @@
+#decl(SHMEM_VEC)
+fn store_shmem(val: vec4<f16>, idx: u32) {
+    shmem[idx] = val.x;
+    shmem[idx + 1] = val.y;
+    shmem[idx + 2] = val.z;
+    shmem[idx + 3] = val.w;
+}
+#enddecl(SHMEM_VEC)
+
+#decl(SHMEM_SCALAR)
+fn store_shmem(val: f16, idx: u32) {
+    shmem[idx] = val;
+}
+#enddecl(SHMEM_SCALAR)
+
+#decl(INIT_SRC0_SHMEM_FLOAT)
+
+fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
+    for (var elem_idx = thread_id * {{VEC_SIZE}}; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE * {{VEC_SIZE}}) {
+        let tile_m = elem_idx / TILE_K;
+        let tile_k = elem_idx % TILE_K;
+        let global_m = offset_m + tile_m;
+        let global_k = k_outer + tile_k;
+        let src0_idx = batch_offset + global_m * params.stride_01 + global_k;
+        let src0_val = select( // taking a slight performance hit to avoid oob
+            {{SRC0_TYPE}}(0.0),
+            src0[src0_idx/{{VEC_SIZE}}],
+            global_m < params.m && global_k < params.k);
+        store_shmem({{SHMEM_TYPE}}(src0_val), elem_idx);
+    }
+}
+
+#enddecl(INIT_SRC0_SHMEM_FLOAT)
+
+#decl(INIT_SRC1_SHMEM)
+
+fn init_shmem_src1(thread_id: u32, batch_offset: u32, offset_n: u32, k_outer: u32) {
+    for (var elem_idx = thread_id * {{VEC_SIZE}}; elem_idx < TILE_SRC1_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE * {{VEC_SIZE}}) {
+        let tile_n = elem_idx / TILE_K;
+        let tile_k = elem_idx % TILE_K;
+        let global_n = offset_n + tile_n;
+        let global_k = k_outer + tile_k;
+        let src1_idx = batch_offset + global_n * params.stride_11 + global_k;
+        let src1_val = select(
+            {{SRC1_TYPE}}(0.0),
+            src1[src1_idx/{{VEC_SIZE}}],
+            global_n < params.n && global_k < params.k);
+        store_shmem({{SHMEM_TYPE}}(src1_val), TILE_SRC0_SHMEM + elem_idx);
+    }
+}
+
+#enddecl(INIT_SRC1_SHMEM)
+
+#decl(INIT_SRC0_SHMEM_Q4_0)
+
+const BLOCK_SIZE = 32u;
+// the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types.
+override BLOCKS_K = TILE_K/BLOCK_SIZE;
+const NQ = 16u;
+const F16_PER_BLOCK = 9u; // 1 scale + 8x4 packed weights
+const WEIGHTS_PER_F16 = 4u; // 4 weights per f16
+const F16_PER_THREAD = NQ / WEIGHTS_PER_F16;
+
+fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
+    for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) {
+        let blck_idx = i / BLOCK_SIZE;
+        let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16;
+        let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u;
+
+        let tile_m = blck_idx / BLOCKS_K;
+        let global_m = offset_m + tile_m;
+        let block_k = blck_idx % BLOCKS_K;
+        let global_k = k_outer / BLOCK_SIZE + block_k;
+
+        if (global_m < params.m && global_k < params.k / BLOCK_SIZE) {
+            let src0_idx = batch_offset + global_m * params.stride_01 + global_k;
+            let scale_idx = src0_idx * F16_PER_BLOCK;
+            let d = src0[scale_idx];
+
+            for (var j = 0u; j < F16_PER_THREAD; j += 2) {
+                let q_0 = src0[scale_idx + 1u + block_offset + j];
+                let q_1 = src0[scale_idx + 1u + block_offset + j + 1];
+
+                let q_packed = bitcast<u32>(vec2(q_0, q_1));
+                for (var k = 0u; k < 4u; k++) {
+                    let q_byte = get_byte(q_packed, k);
+                    let q_hi = (f16((q_byte >> 4) & 0xF) - 8.0) * d;
+                    let q_lo = (f16(q_byte & 0xF) - 8.0) * d;
+                    shmem[shmem_idx + j * 2 + k] = q_lo;
+                    shmem[shmem_idx + j * 2 + k + 16u] = q_hi;
+                }
+            }
+        }
+    }
+}
+
+#enddecl(INIT_SRC0_SHMEM_Q4_0)

ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_reg_tile.tmpl.wgsl

@@ -0,0 +1,247 @@
+#define(VARIANTS)
+[
+  {
+    "SHADER_SUFFIX": "f32_f32_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f32>",
+      "SRC1_TYPE" : "vec4<f32>",
+      "DST_TYPE" : "vec4<f32>",
+      "SHMEM_TYPE" : "vec4<f16>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "SHMEM_VEC", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f32_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f32",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE" : "f32",
+      "SHMEM_TYPE" : "f16",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "SHMEM_SCALAR", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f32_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f16>",
+      "SRC1_TYPE" : "vec4<f32>",
+      "DST_TYPE" : "vec4<f32>",
+      "SHMEM_TYPE" : "vec4<f16>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "SHMEM_VEC", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE" : "f32",
+      "SHMEM_TYPE" : "f16",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "SHMEM_SCALAR", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f16_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f16>",
+      "SRC1_TYPE" : "vec4<f16>",
+      "DST_TYPE" : "vec4<f32>",
+      "SHMEM_TYPE" : "vec4<f16>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "SHMEM_VEC", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f16",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f16",
+      "DST_TYPE" : "f32",
+      "SHMEM_TYPE" : "f16",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "SHMEM_SCALAR", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "q4_0_f32_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "vec4<f32>",
+      "DST_TYPE" : "vec4<f32>",
+      "SHMEM_TYPE" : "vec4<f16>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["BYTE_HELPERS", "VEC", "SHMEM_VEC", "INIT_SRC0_SHMEM_Q4_0", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "q4_0_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE" : "f32",
+      "SHMEM_TYPE" : "f16",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["BYTE_HELPERS", "SCALAR", "SHMEM_SCALAR", "INIT_SRC0_SHMEM_Q4_0", "INIT_SRC1_SHMEM"]
+  }
+]
+
+#end(VARIANTS)
+
+#define(DECLS)
+
+#decl(VEC)
+fn store_val(acc: array<array<f16, TILE_N>, TILE_M>, tn: u32, tm: u32) -> vec4<f32> {
+    return vec4<f32>(f32(acc[tm][tn]), f32(acc[tm + 1][tn]), f32(acc[tm + 2][tn]), f32(acc[tm + 3][tn]));
+}
+#enddecl(VEC)
+
+#decl(SCALAR)
+fn store_val(acc: array<array<f16, TILE_N>, TILE_M>, tn: u32, tm: u32) -> f32 {
+    return f32(acc[tm][tn]);
+}
+#enddecl(SCALAR)
+
+#end(DECLS)
+
+#define(SHADER)
+enable f16;
+
+struct MulMatParams {
+    offset_src0: u32,
+    offset_src1: u32,
+    offset_dst: u32,
+    m: u32,
+    n: u32,
+    k: u32,
+    stride_01: u32,
+    stride_11: u32,
+    stride_02: u32,
+    stride_12: u32,
+    stride_03: u32,
+    stride_13: u32,
+    bs02: u32,
+    bs03: u32,
+    broadcast2: u32,
+    broadcast3: u32
+};
+
+@group(0) @binding(0) var<storage, read_write> src0: array<{{SRC0_TYPE}}>; // M rows, K columns
+@group(0) @binding(1) var<storage, read_write> src1: array<{{SRC1_TYPE}}>; // K rows, N columns (transposed)
+@group(0) @binding(2) var<storage, read_write> dst: array<{{DST_TYPE}}>; // M rows, N columns (transposed)
+
+@group(0) @binding(3) var<uniform> params: MulMatParams;
+
+DECLS
+
+fn get_local_n(thread_id: u32) -> u32 {
+    return thread_id / WORKGROUP_SIZE_M;
+}
+fn get_local_m(thread_id: u32) -> u32 {
+    return thread_id % WORKGROUP_SIZE_M;
+}
+
+// TILE_M must be multiple of 4 for vec4 loads
+const TILE_M = {{WEBGPU_TILE_M}}u;
+const TILE_N = {{WEBGPU_TILE_N}}u;
+
+override WORKGROUP_SIZE_M: u32;
+override WORKGROUP_SIZE_N: u32;
+override TILE_K: u32;
+
+override TOTAL_WORKGROUP_SIZE = WORKGROUP_SIZE_M * WORKGROUP_SIZE_N;
+override TILE_SRC0_SHMEM = TILE_K * WORKGROUP_SIZE_M * TILE_M;
+override TILE_SRC1_SHMEM = TILE_K * WORKGROUP_SIZE_N * TILE_N;
+
+var<workgroup> shmem: array<f16, TILE_SRC0_SHMEM + TILE_SRC1_SHMEM>;
+
+@compute @workgroup_size(TOTAL_WORKGROUP_SIZE)
+fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
+        @builtin(local_invocation_id) local_id: vec3<u32>) {
+
+    let thread_id = local_id.x;
+    let local_m = get_local_m(thread_id);
+    let local_n = get_local_n(thread_id);
+
+    let wg_n_count = (params.n + WORKGROUP_SIZE_N * TILE_N - 1u) / (WORKGROUP_SIZE_N * TILE_N);
+    let wg_m_count = (params.m + WORKGROUP_SIZE_M * TILE_M - 1u) / (WORKGROUP_SIZE_M * TILE_M);
+    let wg_per_matrix = wg_m_count * wg_n_count;
+
+    let batch_idx = wg_id.x / wg_per_matrix;
+
+    let wg_in_batch = wg_id.x % wg_per_matrix;
+    let wg_m = wg_in_batch % wg_m_count;
+    let wg_n = wg_in_batch / wg_m_count;
+
+    let output_row_base = wg_m * WORKGROUP_SIZE_M * TILE_M + local_m * TILE_M;
+    let output_col_base = wg_n * WORKGROUP_SIZE_N * TILE_N + local_n * TILE_N;
+
+    let dst2_stride = params.m * params.n;
+    let dst3_stride = dst2_stride * params.bs02 * params.broadcast2;
+
+    let dst3_idx = batch_idx / (params.bs02 * params.broadcast2);
+    let src03_idx = dst3_idx / params.broadcast3;
+    let src13_idx = dst3_idx;
+    let dst2_idx = batch_idx % (params.bs02 * params.broadcast2);
+    let src02_idx = dst2_idx / params.broadcast2;
+    let src12_idx = dst2_idx;
+
+    let src0_batch_offset = params.offset_src0 + src03_idx * params.stride_03 + src02_idx * params.stride_02;
+    let src1_batch_offset = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12;
+
+    let offset_m = wg_m * WORKGROUP_SIZE_M * TILE_M;
+    let offset_n = wg_n * WORKGROUP_SIZE_N * TILE_N;
+
+    var acc: array<array<f16, TILE_N>, TILE_M>;
+
+    for (var k_outer = 0u; k_outer < params.k; k_outer += TILE_K) {
+
+        // see mul_mat_decls.tmpl
+        init_shmem_src0(thread_id, src0_batch_offset, offset_m, k_outer);
+        init_shmem_src1(thread_id, src1_batch_offset, offset_n, k_outer);
+
+        workgroupBarrier();
+
+        let k_end = min(TILE_K, params.k - k_outer);
+
+        for (var k_inner = 0u; k_inner < k_end; k_inner++) {
+            var src0_tile: array<f16, TILE_M>;
+            for (var tm = 0u; tm < TILE_M; tm++) {
+                let src0_m = local_m * TILE_M + tm;
+                let src0_idx = k_inner + src0_m * TILE_K;
+                src0_tile[tm] = shmem[src0_idx];
+            }
+            for (var tn = 0u; tn < TILE_N; tn++) {
+                let src1_n = local_n * TILE_N + tn;
+                let src1_idx = src1_n * TILE_K + k_inner;
+                let src1_val = shmem[TILE_SRC0_SHMEM + src1_idx];
+                for (var tm = 0u; tm < TILE_M; tm++) {
+                      acc[tm][tn] += src0_tile[tm] * src1_val;
+                }
+            }
+        }
+
+        workgroupBarrier();
+    }
+
+    let dst_batch_offset = params.offset_dst + dst3_idx * dst3_stride + dst2_idx * dst2_stride;
+
+    for (var tn = 0u; tn < TILE_N; tn++) {
+        let global_col = output_col_base + tn;
+        if (global_col < params.n) {
+            for (var tm = 0u; tm < TILE_M; tm += {{VEC_SIZE}}) {
+                let global_row = output_row_base + tm;
+                if (global_row < params.m) {
+                    let dst_idx = dst_batch_offset + global_col * params.m + global_row;
+                    dst[dst_idx/{{VEC_SIZE}}] = store_val(acc, tn, tm);
+                }
+            }
+        }
+    }
+}
+
+#end(SHADER)

ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_subgroup_matrix.tmpl.wgsl

@@ -0,0 +1,302 @@
+#define(VARIANTS)
+[
+  {
+    "SHADER_SUFFIX": "f32_f32_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f32>",
+      "SRC1_TYPE" : "vec4<f32>",
+      "DST_TYPE" : "vec4<f32>",
+      "SHMEM_TYPE" : "vec4<f16>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "SHMEM_VEC", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f32_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f32",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE" : "f32",
+      "SHMEM_TYPE" : "f16",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "SHMEM_SCALAR", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f32_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f16>",
+      "SRC1_TYPE" : "vec4<f32>",
+      "DST_TYPE" : "vec4<f32>",
+      "SHMEM_TYPE" : "vec4<f16>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "SHMEM_VEC", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE" : "f32",
+      "SHMEM_TYPE" : "f16",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "SHMEM_SCALAR", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f16_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f16>",
+      "SRC1_TYPE" : "vec4<f16>",
+      "DST_TYPE" : "vec4<f32>",
+      "SHMEM_TYPE" : "vec4<f16>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "SHMEM_VEC", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f16",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f16",
+      "DST_TYPE" : "f32",
+      "SHMEM_TYPE" : "f16",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "SHMEM_SCALAR", "INIT_SRC0_SHMEM_FLOAT", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "q4_0_f32_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "vec4<f32>",
+      "DST_TYPE" : "vec4<f32>",
+      "SHMEM_TYPE" : "vec4<f16>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["BYTE_HELPERS", "VEC", "SHMEM_VEC", "INIT_SRC0_SHMEM_Q4_0", "INIT_SRC1_SHMEM"]
+  },
+  {
+    "SHADER_SUFFIX": "q4_0_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE" : "f32",
+      "SHMEM_TYPE" : "f16",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["BYTE_HELPERS", "SCALAR", "SHMEM_SCALAR", "INIT_SRC0_SHMEM_Q4_0", "INIT_SRC1_SHMEM"]
+  }
+]
+
+#end(VARIANTS)
+
+#define(DECLS)
+
+#decl(VEC)
+fn store_dst(shmem_idx: u32, dst_idx: u32) {
+    dst[dst_idx] = vec4<f32>(
+        f32(shmem[shmem_idx]),
+        f32(shmem[shmem_idx + 1]),
+        f32(shmem[shmem_idx + 2]),
+        f32(shmem[shmem_idx + 3])
+    );
+}
+#enddecl(VEC)
+
+#decl(SCALAR)
+fn store_dst(shmem_idx: u32, dst_idx: u32) {
+    dst[dst_idx] = f32(shmem[shmem_idx]);
+}
+#enddecl(SCALAR)
+
+#end(DECLS)
+
+#define(SHADER)
+diagnostic(off, chromium.subgroup_matrix_uniformity);
+enable f16;
+enable subgroups;
+enable chromium_experimental_subgroup_matrix;
+
+struct MulMatParams {
+    offset_src0: u32,
+    offset_src1: u32,
+    offset_dst: u32,
+    m: u32,
+    n: u32,
+    k: u32,
+    stride_01: u32,
+    stride_11: u32,
+    stride_02: u32,
+    stride_12: u32,
+    stride_03: u32,
+    stride_13: u32,
+    bs02: u32,
+    bs03: u32,
+    broadcast2: u32,
+    broadcast3: u32
+};
+
+@group(0) @binding(0) var<storage, read_write> src0: array<{{SRC0_TYPE}}>; // M rows, K columns
+@group(0) @binding(1) var<storage, read_write> src1: array<{{SRC1_TYPE}}>; // K rows, N columns (transposed)
+@group(0) @binding(2) var<storage, read_write> dst: array<{{DST_TYPE}}>; // M rows, N columns (transposed)
+
+@group(0) @binding(3) var<uniform> params: MulMatParams;
+
+DECLS
+
+// Note: These are string interpolated at build time, cannot use override constants due to limitations in
+// current Dawn version type definitions/matrix load requirements for constant memory sizes.
+const SUBGROUP_M = {{WEBGPU_SUBGROUP_M}}u;
+const SUBGROUP_N = {{WEBGPU_SUBGROUP_N}}u;
+// For portability we assume the max subgroup size, meaning some subgroups will be masked out if the
+// runtime subgroup size is smaller.
+const MAX_SUBGROUP_SIZE = {{WEBGPU_MAX_SUBGROUP_SIZE}}u;
+
+const EXPECTED_SUBGROUPS = SUBGROUP_M * SUBGROUP_N;
+
+const SUBGROUP_MATRIX_M_SIZE = {{WEBGPU_SG_MAT_M_SIZE}}u;
+const SUBGROUP_MATRIX_N_SIZE = {{WEBGPU_SG_MAT_N_SIZE}}u;
+const SUBGROUP_MATRIX_K_SIZE = {{WEBGPU_SG_MAT_K_SIZE}}u;
+
+const SUBGROUP_MATRIX_M = {{WEBGPU_SUBGROUP_MATRIX_M}}u;
+const SUBGROUP_MATRIX_N = {{WEBGPU_SUBGROUP_MATRIX_N}}u;
+
+const TILE_K = {{WEBGPU_TILE_K}}u;
+
+const WG_M_SG_TILE_SIZE = SUBGROUP_M * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE;
+const WG_N_SG_TILE_SIZE = SUBGROUP_N * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE;
+
+const TOTAL_WORKGROUP_SIZE = SUBGROUP_M * SUBGROUP_N * MAX_SUBGROUP_SIZE;
+const TILE_SRC0_SHMEM = TILE_K * SUBGROUP_M * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE;
+const TILE_SRC1_SHMEM = TILE_K * SUBGROUP_N * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE;
+
+const SG_MAT_ACCUM_SHMEM = SUBGROUP_M * SUBGROUP_MATRIX_M * SUBGROUP_N * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_M_SIZE * SUBGROUP_MATRIX_N_SIZE;
+
+// We reuse shmem for accumulation matrices
+const SHMEM_SIZE = max(TILE_SRC0_SHMEM + TILE_SRC1_SHMEM, SG_MAT_ACCUM_SHMEM);
+
+var<workgroup> shmem: array<f16, SHMEM_SIZE>;
+
+@compute @workgroup_size(TOTAL_WORKGROUP_SIZE)
+fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
+        @builtin(local_invocation_id) local_id: vec3<u32>,
+        @builtin(subgroup_id) subgroup_id: u32) {
+
+    let thread_id = local_id.x;
+    let subgroup_m = subgroup_id % SUBGROUP_M;
+    let subgroup_n = subgroup_id / SUBGROUP_M;
+
+    let wg_m_count = (params.m + WG_M_SG_TILE_SIZE - 1) / WG_M_SG_TILE_SIZE;
+    let wg_n_count = (params.n + WG_N_SG_TILE_SIZE - 1) / WG_N_SG_TILE_SIZE;
+    let wg_per_matrix = wg_m_count * wg_n_count;
+
+    let batch_idx = wg_id.x / wg_per_matrix;
+
+    let wg_in_batch = wg_id.x % wg_per_matrix;
+    let wg_m = wg_in_batch % wg_m_count;
+    let wg_n = wg_in_batch / wg_m_count;
+
+    let dst2_stride = params.m * params.n;
+    let dst3_stride = dst2_stride * params.bs02 * params.broadcast2;
+
+    let dst3_idx = batch_idx / (params.bs02 * params.broadcast2);
+    let src03_idx = dst3_idx / params.broadcast3;
+    let src13_idx = dst3_idx;
+    let dst2_idx = batch_idx % (params.bs02 * params.broadcast2);
+    let src02_idx = dst2_idx / params.broadcast2;
+    let src12_idx = dst2_idx;
+
+    let src0_batch_offset = params.offset_src0 + src03_idx * params.stride_03 + src02_idx * params.stride_02;
+    let src1_batch_offset = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12;
+
+    let offset_m = wg_m * SUBGROUP_M * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE;
+    let offset_n = wg_n * SUBGROUP_N * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE;
+
+    var acc_sg_mat : array<array<subgroup_matrix_result<f16, SUBGROUP_MATRIX_N_SIZE, SUBGROUP_MATRIX_M_SIZE>, SUBGROUP_MATRIX_N>, SUBGROUP_MATRIX_M>;
+
+    for (var k_outer = 0u; k_outer < params.k; k_outer += TILE_K) {
+
+        // see mul_mat_decls.tmpl
+        init_shmem_src0(thread_id, src0_batch_offset, offset_m, k_outer);
+        init_shmem_src1(thread_id, src1_batch_offset, offset_n, k_outer);
+
+        workgroupBarrier();
+
+        if (subgroup_id < EXPECTED_SUBGROUPS) {
+
+            for (var k_inner = 0u; k_inner < TILE_K; k_inner += SUBGROUP_MATRIX_K_SIZE) {
+
+                let src0_shmem_idx_base = subgroup_m * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE * TILE_K + k_inner;
+                var src0_sg_mats: array<subgroup_matrix_left<f16, SUBGROUP_MATRIX_K_SIZE, SUBGROUP_MATRIX_M_SIZE>, SUBGROUP_MATRIX_M>;
+                for (var m = 0u; m < SUBGROUP_MATRIX_M; m++) {
+                    src0_sg_mats[m] = subgroupMatrixLoad<subgroup_matrix_left<f16, SUBGROUP_MATRIX_K_SIZE, SUBGROUP_MATRIX_M_SIZE>>(
+                        &shmem,
+                        src0_shmem_idx_base + m * SUBGROUP_MATRIX_M_SIZE * TILE_K,
+                        false,
+                        TILE_K
+                    );
+                }
+
+                let src1_shmem_idx_base = TILE_SRC0_SHMEM + subgroup_n * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE * TILE_K + k_inner;
+                for (var n = 0u; n < SUBGROUP_MATRIX_N; n++) {
+                    let src1_sg_mat = subgroupMatrixLoad<subgroup_matrix_right<f16, SUBGROUP_MATRIX_N_SIZE, SUBGROUP_MATRIX_K_SIZE>>(
+                        &shmem,
+                        src1_shmem_idx_base + n * SUBGROUP_MATRIX_N_SIZE * TILE_K,
+                        true,
+                        TILE_K
+                    );
+                    for (var m = 0u; m < SUBGROUP_MATRIX_M; m++) {
+                        acc_sg_mat[m][n] = subgroupMatrixMultiplyAccumulate(src0_sg_mats[m], src1_sg_mat, acc_sg_mat[m][n]);
+                    }
+                }
+            }
+        }
+
+        workgroupBarrier();
+    }
+
+    let dst_batch_offset = params.offset_dst + dst3_idx * dst3_stride + dst2_idx * dst2_stride;
+
+    // Stage the subgroup matrix tiles into shared memory
+    // This uses WG_M_SG_TILE_SIZE as the stride (number of columns in the workgroup tile).
+    let WG_TILE_STRIDE = WG_M_SG_TILE_SIZE;
+    let tile_row_base_local = subgroup_n * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE;
+    let tile_col_base_local = subgroup_m * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE;
+
+    if (subgroup_id < EXPECTED_SUBGROUPS) { // 2-5% performance hit :(
+        for (var n = 0u; n < SUBGROUP_MATRIX_N; n++) {
+            for (var m = 0u; m < SUBGROUP_MATRIX_M; m++) {
+                let local_row = tile_row_base_local + n * SUBGROUP_MATRIX_N_SIZE;
+                let local_col = tile_col_base_local + m * SUBGROUP_MATRIX_M_SIZE;
+                let out_base = local_row * WG_TILE_STRIDE + local_col;
+                subgroupMatrixStore(&shmem, out_base, acc_sg_mat[m][n], true, WG_TILE_STRIDE);
+            }
+        }
+    }
+
+    workgroupBarrier();
+
+    // Cooperative write: iterate over the entire workgroup tile
+    let tile_rows = WG_N_SG_TILE_SIZE;
+    let tile_cols = WG_M_SG_TILE_SIZE;
+    let total_tile_elems = tile_rows * tile_cols;
+    let tile_dst_row_base = wg_m * SUBGROUP_M * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE;
+    let tile_dst_col_base = wg_n * SUBGROUP_N * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE;
+
+    for (var idx = thread_id * {{VEC_SIZE}}; idx < total_tile_elems; idx += TOTAL_WORKGROUP_SIZE * {{VEC_SIZE}}) {
+        let local_row = idx % WG_TILE_STRIDE;
+        let local_col = idx / WG_TILE_STRIDE;
+
+        let global_row = tile_dst_row_base + local_row;
+        let global_col = tile_dst_col_base + local_col;
+
+        if (global_col < params.n && global_row < params.m) {
+            let dst_idx = dst_batch_offset + global_col * params.m + global_row;
+            store_dst(idx, dst_idx/{{VEC_SIZE}});
+        }
+    }
+}
+
+#end(SHADER)

ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.tmpl.wgsl

@@ -0,0 +1,267 @@
+#define(VARIANTS)
+[
+  {
+    "SHADER_SUFFIX": "f32_f32_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f32>",
+      "SRC1_TYPE" : "vec4<f32>",
+      "DST_TYPE": "vec4<f32>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "MUL_ACC_FLOAT"]
+  },
+  {
+    "SHADER_SUFFIX": "f32_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f32",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE": "f32",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "MUL_ACC_FLOAT"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f32_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f16>",
+      "SRC1_TYPE" : "vec4<f32>",
+      "DST_TYPE": "vec4<f32>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "MUL_ACC_FLOAT"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE": "f32",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "MUL_ACC_FLOAT"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f16_vec",
+    "REPLS": {
+      "SRC0_TYPE" : "vec4<f16>",
+      "SRC1_TYPE" : "vec4<f16>",
+      "DST_TYPE": "vec4<f32>",
+      "VEC_SIZE" : 4,
+    },
+    "DECLS": ["VEC", "MUL_ACC_FLOAT"]
+  },
+  {
+    "SHADER_SUFFIX": "f16_f16",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f16",
+      "DST_TYPE": "f32",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["SCALAR", "MUL_ACC_FLOAT"]
+  },
+  {
+    "SHADER_SUFFIX": "q4_0_f32",
+    "REPLS": {
+      "SRC0_TYPE" : "f16",
+      "SRC1_TYPE" : "f32",
+      "DST_TYPE": "f32",
+      "VEC_SIZE" : 1,
+    },
+    "DECLS": ["BYTE_HELPERS", "SCALAR", "MUL_ACC_Q4_0"]
+  }
+]
+
+#end(VARIANTS)
+
+#define(DECLS)
+
+#decl(VEC)
+fn inner_dot(src0_val: {{SRC0_TYPE}}, src1_val: {{SRC1_TYPE}}) -> f32 {
+    return f32(dot({{SRC1_TYPE}}(src0_val), src1_val));
+}
+
+fn store_val(group_base: u32) -> vec4<f32> {
+    return vec4<f32>(partial_sums[group_base],
+                     partial_sums[group_base + THREADS_PER_OUTPUT],
+                     partial_sums[group_base + THREADS_PER_OUTPUT * 2],
+                     partial_sums[group_base + THREADS_PER_OUTPUT * 3]);
+}
+#enddecl(VEC)
+
+#decl(SCALAR)
+fn inner_dot(src0_val: {{SRC0_TYPE}}, src1_val: {{SRC1_TYPE}}) -> f32 {
+    return f32(src0_val) * f32(src1_val);
+}
+
+fn store_val(group_base: u32) -> f32 {
+    return partial_sums[group_base];
+}
+#enddecl(SCALAR)
+
+#decl(MUL_ACC_FLOAT)
+
+fn mul_acc(tig:u32, tile_size: u32, idx_base: u32, k_outer: u32) -> f32 {
+    var local_sum = 0.0;
+    for (var i = tig * {{VEC_SIZE}}; i < tile_size; i += THREADS_PER_OUTPUT * {{VEC_SIZE}}) {
+        let a = src0[(idx_base + k_outer + i) / {{VEC_SIZE}}];
+        let b = shared_vector[i / {{VEC_SIZE}}];
+        local_sum += inner_dot(a, b);
+    }
+    return local_sum;
+}
+
+#enddecl(MUL_ACC_FLOAT)
+
+#decl(MUL_ACC_Q4_0)
+
+const BLOCK_SIZE = 32;
+const NQ = 16u; // number of weights per thread
+const F16_PER_BLOCK = 9u; // 1 scale + 8x4 packed weights
+const WEIGHTS_PER_F16 = 4u; // 4 weights per f16
+const F16_PER_THREAD = NQ / WEIGHTS_PER_F16;
+
+fn mul_acc(tig:u32, tile_size: u32, idx_base: u32, k_outer: u32) -> f32 {
+    var local_sum = 0.0;
+    for (var i = tig * NQ; i < tile_size; i += THREADS_PER_OUTPUT * NQ) {
+        let blck_idx = i / BLOCK_SIZE;
+        let block_offset = (i % BLOCK_SIZE) / WEIGHTS_PER_F16;
+        let scale_idx = (idx_base + k_outer / BLOCK_SIZE + blck_idx) * F16_PER_BLOCK;
+        // each f16 contains offsets [block_offset, block_offset + 1] and [block_offset + 16, block_offset + 17]
+        let shmem_idx = blck_idx * BLOCK_SIZE + block_offset * 2u;
+        let d = f32(src0[scale_idx]);
+        for (var j = 0u; j < F16_PER_THREAD; j += 2) {
+            let q_0 = src0[scale_idx + 1 + block_offset + j];
+            let q_1 = src0[scale_idx + 1 + block_offset + j + 1];
+            let q_packed = bitcast<u32>(vec2(q_0, q_1));
+            for (var k: u32 = 0; k < 4; k++) {
+                let q_byte = get_byte(q_packed, k);
+                let q_hi = (f32((q_byte >> 4) & 0xF) - 8.0) * d;
+                let q_lo = (f32(q_byte & 0xF) - 8.0) * d;
+                local_sum += q_lo * shared_vector[shmem_idx + j * 2 + k];
+                local_sum += q_hi * shared_vector[shmem_idx + j * 2 + k + 16];
+            }
+        }
+    }
+    return local_sum;
+}
+
+#enddecl(MUL_ACC_Q4_0)
+
+#end(DECLS)
+
+#define(SHADER)
+enable f16;
+
+DECLS
+
+struct MulMatParams {
+    offset_src0: u32,
+    offset_src1: u32,
+    offset_dst: u32,
+    m: u32,
+    n: u32,
+    k: u32,
+    stride_01: u32,
+    stride_11: u32,
+    stride_02: u32,
+    stride_12: u32,
+    stride_03: u32,
+    stride_13: u32,
+    bs02: u32,
+    bs03: u32,
+    broadcast2: u32,
+    broadcast3: u32
+};
+
+@group(0) @binding(0) var<storage, read_write> src0: array<{{SRC0_TYPE}}>; // Matrix (M x K)
+@group(0) @binding(1) var<storage, read_write> src1: array<{{SRC1_TYPE}}>; // Vector (K x 1, transposed)
+@group(0) @binding(2) var<storage, read_write> dst: array<{{DST_TYPE}}>;  // Result vector (transposed)
+
+@group(0) @binding(3) var<uniform> params: MulMatParams;
+
+override WORKGROUP_SIZE: u32;
+override TILE_K: u32;
+override OUTPUTS_PER_WG: u32;
+override THREADS_PER_OUTPUT = WORKGROUP_SIZE / OUTPUTS_PER_WG;
+
+// Shared memory for collaborative loading and reduction
+var<workgroup> shared_vector: array<{{SRC1_TYPE}}, TILE_K/{{VEC_SIZE}}>;  // Cache vector tile
+var<workgroup> partial_sums: array<f32, WORKGROUP_SIZE>;   // For reduction
+
+@compute @workgroup_size(WORKGROUP_SIZE)
+fn main(
+    @builtin(local_invocation_id) local_id: vec3<u32>,
+    @builtin(workgroup_id) wg_id: vec3<u32>,
+    @builtin(num_workgroups) num_wg: vec3<u32>) {
+    let thread_id = local_id.x;
+
+    // Handle batch dimensions
+    let total_batches = params.bs02 * params.broadcast2 * params.bs03 * params.broadcast3;
+    let wg_linear = wg_id.y * num_wg.x + wg_id.x;
+    let output_groups = (params.m + OUTPUTS_PER_WG - 1u) / OUTPUTS_PER_WG;
+    let batch_idx = wg_linear / output_groups;
+    if (batch_idx >= total_batches) {
+        return;
+    }
+
+    // Which of the outputs does this thread belong to?
+    let thread_group = thread_id / THREADS_PER_OUTPUT;
+    let thread_in_group = thread_id % THREADS_PER_OUTPUT;
+
+    // Each workgroup computes OUTPUTS_PER_WG consecutive outputs
+    let output_row = (wg_linear % output_groups) * OUTPUTS_PER_WG + thread_group;
+
+    let dst2_stride = params.m * params.n;
+    let dst2_idx = batch_idx % (params.bs02 * params.broadcast2);
+    let dst3_stride = dst2_stride * params.bs02 * params.broadcast2;
+    let dst3_idx = batch_idx / (params.bs02 * params.broadcast2);
+    let src03_idx = dst3_idx / params.broadcast3;
+    let src13_idx = dst3_idx;
+    let src02_idx = dst2_idx / params.broadcast2;
+    let src12_idx = dst2_idx;
+
+    let src0_idx_base = params.offset_src0 + src03_idx * params.stride_03 + src02_idx * params.stride_02 + output_row * params.stride_01;
+    let src1_idx_base = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12;
+    let dst_idx = params.offset_dst + dst3_idx * dst3_stride + dst2_idx * dst2_stride + output_row;
+
+    var local_sum = 0.0;
+
+    // Each thread processes multiple K elements and accumulates
+    for (var k_tile = 0u; k_tile < params.k; k_tile += TILE_K) {
+        let tile_size = min(TILE_K, params.k - k_tile);
+
+        // Cooperatively load vector tile into shared memory (all threads)
+        for (var i = thread_id * {{VEC_SIZE}}; i < tile_size; i += WORKGROUP_SIZE * {{VEC_SIZE}}) {
+            shared_vector[i / {{VEC_SIZE}}] = src1[(src1_idx_base + k_tile + i) / {{VEC_SIZE}}];
+        }
+
+        workgroupBarrier();
+
+        if (output_row < params.m) {
+            local_sum += mul_acc(thread_in_group, tile_size, src0_idx_base, k_tile);
+        }
+
+        workgroupBarrier();
+    }
+
+    // Store partial sums and reduce within each partition
+    partial_sums[thread_id] = local_sum;
+    workgroupBarrier();
+    let group_base = thread_group * THREADS_PER_OUTPUT;
+    let thread_base = group_base + thread_in_group;
+    var offset = THREADS_PER_OUTPUT / 2;
+    while (offset > 0) {
+        if (thread_in_group < offset) {
+            partial_sums[thread_base] += partial_sums[thread_base + offset];
+        }
+        offset = offset / 2;
+        workgroupBarrier();
+    }
+
+    // Store back to global memory
+    if (output_row < params.m && thread_group % {{VEC_SIZE}} == 0 && thread_in_group == 0) {
+        dst[dst_idx / {{VEC_SIZE}}] = store_val(group_base);
+    }
+}
+#end(SHADER)

gguf-py/gguf/lazy.py

@@ -48,13 +48,18 @@ class LazyMeta(ABCMeta):
         # NOTE: doing this from a metaclass is very convenient
         # TODO: make this even more comprehensive
         for binary_op in (
-            "lt", "le", "eq", "ne", "ge", "gt", "not"
-            "abs", "add", "and", "floordiv", "invert", "lshift", "mod", "mul", "matmul",
-            "neg", "or", "pos", "pow", "rshift", "sub", "truediv", "xor",
+            "lt", "le", "eq", "ne", "ge", "gt",
+            "add", "and", "floordiv", "lshift", "mod", "mul", "matmul",
+            "or", "pow", "rshift", "sub", "truediv", "xor",
             "iadd", "iand", "ifloordiv", "ilshift", "imod", "imul", "ior", "irshift", "isub", "ixor",
             "radd", "rand", "rfloordiv", "rmul", "ror", "rpow", "rsub", "rtruediv", "rxor",
         ):
             attr_name = f"__{binary_op}__"
+            # evaluation on the meta tensor is needed in case there's broadcasting
+            namespace[attr_name] = mk_wrap(attr_name, meta_noop=False)
+
+        for unary_op in ("not", "abs", "invert", "neg", "pos"):
+            attr_name = f"__{unary_op}__"
             # the result of these operators usually has the same shape and dtype as the input,
             # so evaluation on the meta tensor can be skipped.
             namespace[attr_name] = mk_wrap(attr_name, meta_noop=True)

gguf-py/gguf/utility.py

@@ -1,10 +1,12 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
+from pathlib import Path
 from typing import Literal
 
 import os
 import json
+import numpy as np
 
 
 def fill_templated_filename(filename: str, output_type: str | None) -> str:
@@ -177,6 +179,10 @@ class SafetensorRemote:
             except KeyError as e:
                 raise ValueError(f"Missing key in metadata for tensor '{name}': {e}, meta = {meta}")
 
+        # order by name (same as default safetensors behavior)
+        # ref: https://github.com/huggingface/safetensors/blob/0816a1ae1d6b731cefd67f061d80d1cadd0dd7bb/bindings/python/src/lib.rs#L606
+        res = dict(sorted(res.items(), key=lambda t: t[0]))
+
         return res
 
     @classmethod
@@ -266,3 +272,77 @@ class SafetensorRemote:
         if os.environ.get("HF_TOKEN"):
             headers["Authorization"] = f"Bearer {os.environ['HF_TOKEN']}"
         return headers
+
+
+@dataclass
+class LocalTensorRange:
+    filename: Path
+    offset: int
+    size: int
+
+
+@dataclass
+class LocalTensor:
+    dtype: str
+    shape: tuple[int, ...]
+    data_range: LocalTensorRange
+
+    def mmap_bytes(self) -> np.ndarray:
+        return np.memmap(self.data_range.filename, offset=self.data_range.offset, shape=self.data_range.size)
+
+
+class SafetensorsLocal:
+    """
+        Read a safetensors file from the local filesystem.
+
+        Custom parsing gives a bit more control over the memory usage.
+        The official safetensors library doesn't expose file ranges.
+    """
+    ALIGNMENT = 8  # bytes
+
+    tensors: dict[str, LocalTensor]
+
+    def __init__(self, filename: Path):
+        with open(filename, "rb") as f:
+            metadata_length = int.from_bytes(f.read(8), byteorder='little')
+            file_size = os.stat(filename).st_size
+            if file_size < 8 + metadata_length:
+                raise ValueError(f"Could not read complete metadata. Need {8 + metadata_length} bytes, got {file_size}")
+
+            metadata_str = f.read(metadata_length).decode('utf-8')
+            try:
+                metadata = json.loads(metadata_str)
+            except json.JSONDecodeError as e:
+                raise ValueError(f"Failed to parse safetensors metadata as JSON: {e}")
+
+            data_start_offset = f.tell()
+            alignment = self.ALIGNMENT
+            if data_start_offset % alignment != 0:
+                data_start_offset += alignment - (data_start_offset % alignment)
+
+            tensors: dict[str, LocalTensor] = {}
+            for name, meta in metadata.items():
+                if name == "__metadata__":
+                    # ignore metadata, it's not a tensor
+                    continue
+
+                tensors[name] = LocalTensor(
+                    dtype=meta["dtype"],
+                    shape=tuple(meta["shape"]),
+                    data_range=LocalTensorRange(
+                        filename,
+                        data_start_offset + meta["data_offsets"][0],
+                        meta["data_offsets"][1] - meta["data_offsets"][0],
+                    ),
+                )
+
+            # order by name (same as default safetensors behavior)
+            # ref: https://github.com/huggingface/safetensors/blob/0816a1ae1d6b731cefd67f061d80d1cadd0dd7bb/bindings/python/src/lib.rs#L606
+            self.tensors = dict(sorted(tensors.items(), key=lambda t: t[0]))
+
+    def __enter__(self, *args, **kwargs):
+        del args, kwargs  # unused
+        return self.tensors
+
+    def __exit__(self, *args, **kwargs):
+        del args, kwargs  # unused

include/llama.h

@@ -463,6 +463,7 @@ extern "C" {
 
     // NOTE: After creating a llama_context, it is recommended to query the actual values using these functions
     //       In some cases the requested values via llama_context_params may differ from the actual values used by the context
+    //       ref: https://github.com/ggml-org/llama.cpp/pull/17046#discussion_r2503085732
     LLAMA_API uint32_t llama_n_ctx      (const struct llama_context * ctx);
     LLAMA_API uint32_t llama_n_ctx_seq  (const struct llama_context * ctx);
     LLAMA_API uint32_t llama_n_batch    (const struct llama_context * ctx);
@@ -485,6 +486,7 @@ extern "C" {
 
     LLAMA_API int32_t llama_model_n_ctx_train(const struct llama_model * model);
     LLAMA_API int32_t llama_model_n_embd     (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_embd_inp (const struct llama_model * model);
     LLAMA_API int32_t llama_model_n_layer    (const struct llama_model * model);
     LLAMA_API int32_t llama_model_n_head     (const struct llama_model * model);
     LLAMA_API int32_t llama_model_n_head_kv  (const struct llama_model * model);

src/llama-context.cpp

@@ -114,10 +114,14 @@ llama_context::llama_context(
         }
     }
 
+    // ref: https://github.com/ggml-org/llama.cpp/pull/17046#discussion_r2503085732
+    cparams.n_ctx = GGML_PAD(cparams.n_ctx, 256);
+
     if (cparams.kv_unified) {
         cparams.n_ctx_seq = cparams.n_ctx;
     } else {
         cparams.n_ctx_seq = cparams.n_ctx / cparams.n_seq_max;
+        cparams.n_ctx_seq = GGML_PAD(cparams.n_ctx_seq, 256);
 
         if (cparams.n_ctx_seq == 0) {
             throw std::runtime_error("n_ctx_seq == 0");
@@ -823,7 +827,7 @@ int llama_context::encode(const llama_batch & batch_inp) {
 
     const auto & hparams = model.hparams;
 
-    const int64_t n_embd  = hparams.n_embd;
+    const int64_t n_embd  = hparams.n_embd_inp();
     const int64_t n_vocab = model.vocab.n_tokens();
 
     // note: during encode, we always pass the full sequence starting from pos = 0
@@ -992,7 +996,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
     const auto & hparams = model.hparams;
 
     const int64_t n_vocab = vocab.n_tokens();
-    const int64_t n_embd  = hparams.n_embd;
+    const int64_t n_embd  = hparams.n_embd_inp();
 
     // when computing embeddings, all tokens are output
     const bool output_all = cparams.embeddings;
@@ -2150,7 +2154,7 @@ void llama_context::opt_epoch_iter(
             batch.logits  [pos_batch]    = true;
         }
 
-        if (!balloc->init(batch, model.vocab, nullptr, model.hparams.n_embd, cparams.kv_unified ? LLAMA_MAX_SEQ : cparams.n_seq_max, true)) {
+        if (!balloc->init(batch, model.vocab, nullptr, model.hparams.n_embd_inp(), cparams.kv_unified ? LLAMA_MAX_SEQ : cparams.n_seq_max, true)) {
             LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
             return;
         }

src/llama-graph.cpp

@@ -1142,7 +1142,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
 
 // input embeddings with optional lora
 ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
-    const int64_t n_embd = hparams.n_embd;
+    const int64_t n_embd = hparams.n_embd_inp();
 
     auto inp = std::make_unique<llm_graph_input_embd>();
 
@@ -1279,7 +1279,7 @@ ggml_tensor * llm_graph_context::build_inp_cross_embd() const {
     //    return cur;
     //}
 
-    const auto n_embd = !cross->v_embd.empty() ? cross->n_embd : hparams.n_embd;
+    const auto n_embd = !cross->v_embd.empty() ? cross->n_embd : hparams.n_embd_inp();
     const auto n_enc  = !cross->v_embd.empty() ? cross->n_enc : hparams.n_ctx_train;
 
     cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_enc);

src/llama-hparams.cpp

@@ -60,6 +60,16 @@ uint32_t llama_hparams::n_gqa(uint32_t il) const {
     return n_head/n_head_kv;
 }
 
+uint32_t llama_hparams::n_embd_inp() const {
+    uint32_t n_embd_inp = n_embd;
+
+    if (n_deepstack_layers > 0) {
+        n_embd_inp += n_embd * n_deepstack_layers;
+    }
+
+    return n_embd_inp;
+}
+
 uint32_t llama_hparams::n_embd_k_gqa(uint32_t il) const {
     const uint32_t n_head_kv = this->n_head_kv(il);
 

src/llama-hparams.h

@@ -227,6 +227,9 @@ struct llama_hparams {
 
     uint32_t n_gqa(uint32_t il = 0) const;
 
+    // dimension of main + auxiliary input embeddings
+    uint32_t n_embd_inp() const;
+
     // dimension of key embeddings across all k-v heads
     uint32_t n_embd_k_gqa(uint32_t il = 0) const;
 

src/llama-kv-cache-iswa.cpp

@@ -45,7 +45,9 @@ llama_kv_cache_iswa::llama_kv_cache_iswa(
 
     const uint32_t size_base = kv_size;
 
-    uint32_t size_swa = std::min(size_base, GGML_PAD(hparams.n_swa*(unified ? n_seq_max : 1) + n_ubatch, n_pad));
+    // note: the SWA cache is always padded to 256 for performance
+    //       https://github.com/ggml-org/llama.cpp/issues/17037
+    uint32_t size_swa = GGML_PAD(std::min(size_base, hparams.n_swa*(unified ? n_seq_max : 1) + n_ubatch), 256);
 
     // when using full-size SWA cache, we set the SWA cache size to be equal to the base cache size
     if (swa_full) {

src/llama-model.cpp

@@ -276,8 +276,8 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w
             } break;
         case GGML_OP_IM2COL:
             {
-                const int n_embd = hparams.n_embd;
-                ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_embd, w->ne[1], 1, 1);
+                const int n_embd_inp = hparams.n_embd_inp();
+                ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_embd_inp, w->ne[1], 1, 1);
                 op_tensor = ggml_im2col(ctx, w, b, 1, 0, 0, 0, 1, 0, false, GGML_TYPE_F16);
             } break;
         case GGML_OP_SCALE:
@@ -1039,9 +1039,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     case 64: type = LLM_TYPE_32B; break;
                     default: type = LLM_TYPE_UNKNOWN;
                 }
-                // since vision model stacks deepstack features along feature dim
-                // we also create a fake "n_embd" for text model to be the main embd + deepstack embds
-                hparams.n_embd *= hparams.n_deepstack_layers + 1;
             } break;
         case LLM_ARCH_QWEN3MOE:
             {
@@ -1065,9 +1062,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     case 94: type = LLM_TYPE_235B_A22B; break;
                     default: type = LLM_TYPE_UNKNOWN;
                 }
-                // since vision model stacks deepstack features along feature dim
-                // we also create a fake "n_embd" for text model to be the main embd + deepstack embds
-                hparams.n_embd *= hparams.n_deepstack_layers + 1;
             } break;
         case LLM_ARCH_PHI2:
             {
@@ -3341,10 +3335,6 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
             case LLM_ARCH_QWEN3:
             case LLM_ARCH_QWEN3VL:
                 {
-                    // for model loading, the weights only have the main embd
-                    // so we need to divide by the number of deepstack layers + 1
-                    // n_embd is const int so we declare a new variable
-                    int64_t n_embd = hparams.n_embd / (hparams.n_deepstack_layers + 1);
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
 
                     // output
@@ -3380,10 +3370,6 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
             case LLM_ARCH_QWEN3MOE:
             case LLM_ARCH_QWEN3VLMOE:
                 {
-                    // for model loading, the weights only have the main embd
-                    // so we need to divide by the number of deepstack layers + 1
-                    // n_embd is const int so we declare a new variable
-                    int64_t n_embd = hparams.n_embd / (hparams.n_deepstack_layers + 1);
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
 
                     // output
@@ -6535,6 +6521,7 @@ void llama_model::print_info() const {
     if (!hparams.vocab_only) {
         LLAMA_LOG_INFO("%s: n_ctx_train      = %u\n",     __func__, hparams.n_ctx_train);
         LLAMA_LOG_INFO("%s: n_embd           = %u\n",     __func__, hparams.n_embd);
+        LLAMA_LOG_INFO("%s: n_embd_inp       = %u\n",     __func__, hparams.n_embd_inp());
         LLAMA_LOG_INFO("%s: n_layer          = %u\n",     __func__, hparams.n_layer);
         LLAMA_LOG_INFO("%s: n_head           = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head(il);    }, hparams.n_layer).c_str());
         LLAMA_LOG_INFO("%s: n_head_kv        = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str());
@@ -7380,6 +7367,10 @@ int32_t llama_model_n_embd(const llama_model * model) {
     return model->hparams.n_embd;
 }
 
+int32_t llama_model_n_embd_inp(const llama_model * model) {
+    return model->hparams.n_embd_inp();
+}
+
 int32_t llama_model_n_layer(const llama_model * model) {
     return model->hparams.n_layer;
 }

src/models/qwen3vl-moe.cpp

@@ -1,9 +1,8 @@
 #include "models.h"
 
 llm_build_qwen3vlmoe::llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-    const int64_t n_embd_full = hparams.n_embd; // main embd + deepstack embds
     const size_t n_deepstack_layers = hparams.n_deepstack_layers;
-    const int64_t n_embd = n_embd_full / (n_deepstack_layers + 1);
+    const int64_t n_embd = hparams.n_embd;
     const int64_t n_embd_head = hparams.n_embd_head_v;
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

src/models/qwen3vl.cpp

@@ -1,13 +1,10 @@
 #include "models.h"
 
 llm_build_qwen3vl::llm_build_qwen3vl(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-
-    const int64_t n_embd_full = hparams.n_embd; // main embd + deepstack embds
     const size_t n_deepstack_layers = hparams.n_deepstack_layers;
-    const int64_t n_embd = n_embd_full / (n_deepstack_layers + 1);
+    const int64_t n_embd = hparams.n_embd;
     const int64_t n_embd_head = hparams.n_embd_head_v;
 
-
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
     GGML_ASSERT(n_embd_head == hparams.n_rot);
 

tests/test-backend-ops.cpp

@@ -272,6 +272,10 @@ static double mean_abs_asymm(const float * a, const float * b, const size_t n, c
 
 // utils for printing the variables of the test cases
 
+static std::string var_to_str(const std::string & x) {
+    return x;
+}
+
 template<typename T>
 static std::string var_to_str(const T & x) {
     return std::to_string(x);
@@ -323,7 +327,8 @@ static std::string var_to_str(ggml_scale_mode mode) {
     switch (mode) {
         case GGML_SCALE_MODE_NEAREST:  return "nearest";
         case GGML_SCALE_MODE_BILINEAR: return "bilinear";
-        default:                      return std::to_string(mode);
+        case GGML_SCALE_MODE_BICUBIC:  return "bicubic";
+        default:                       return std::to_string(mode);
     }
 }
 
@@ -2294,6 +2299,79 @@ struct test_rope_set_rows : public test_case {
     }
 };
 
+// GGML_OP_RMS_NORM + GGML_OP_MUL + GGML_OP_ROPE (+ GGML_OP_VIEW + GGML_OP_SET_ROWS)
+struct test_rms_norm_mul_rope : public test_case {
+    const std::array<int64_t, 4> ne;
+    const float eps;
+    const bool multi_add; // test a sequence of adds feeding into rms_norm
+    const bool set_rows;
+    int mode;
+
+    std::string op_desc(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return "RMS_NORM_MUL_ROPE";
+    }
+
+    bool run_whole_graph() override { return true; }
+
+    std::string vars() override {
+        return VARS_TO_STR5(ne, eps, multi_add, set_rows, mode);
+    }
+
+    test_rms_norm_mul_rope(std::array<int64_t, 4> ne, float eps = 1e-6f, bool multi_add = false,
+                           bool set_rows = false, int mode = GGML_ROPE_TYPE_NORMAL)
+        : ne(ne), eps(eps), multi_add(multi_add), set_rows(set_rows), mode(mode) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, ne[0], ne[1], ne[2], 1);
+        ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, ne[0], ne[1], ne[2], 1);
+        ggml_tensor * c = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, ne[0], ne[1], ne[2], 1);
+
+        if (multi_add) {
+            a = ggml_add(ctx, ggml_add(ctx, a, b), c);
+        }
+
+        a = ggml_mul(ctx, ggml_rms_norm(ctx, a, eps), b);
+
+        ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne[2]);
+
+        ggml_tensor * rope = ggml_rope(ctx, a, pos, ne[0], mode);
+
+        ggml_tensor * out;
+
+        if (set_rows) {
+            ggml_tensor * view = ggml_view_2d(ctx, rope, ne[0] * ne[1], ne[2], rope->nb[2], 0);
+
+            ggml_tensor * dst = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, ne[0] * ne[1], ne[2] * ne[3], 1, 1);
+            ggml_set_name(dst, "dst");
+
+            ggml_tensor * row_idxs = ggml_new_tensor_3d(ctx, GGML_TYPE_I64, ne[2], 1, 1);
+            ggml_set_name(row_idxs, "row_idxs");
+
+            out = ggml_set_rows(ctx, dst, view, row_idxs);
+            ggml_set_name(out, "out");
+        } else {
+            out = rope;
+        }
+
+        return out;
+    }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+            if (t->type == GGML_TYPE_I64 || t->type == GGML_TYPE_I32) {
+                if (ggml_is_view_op(t->op)) {
+                    continue;
+                }
+
+                init_set_rows_row_ids(t, ne[2]);
+            } else {
+                init_tensor_uniform(t);
+            }
+        }
+    }
+};
+
 // GGML_OP_ARGMAX
 struct test_argmax : public test_case {
     const ggml_type type;
@@ -3484,6 +3562,27 @@ struct test_mul_mat : public test_case {
     }
 };
 
+static void init_mul_mat_id_tensors(ggml_context * ctx, int n_mats) {
+    std::random_device rd;
+    std::default_random_engine rng(rd());
+    for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+        if (t->type == GGML_TYPE_I32) {
+            if (ggml_is_view_op(t->op)) { continue; }
+            // ids
+            for (int64_t r = 0; r < ggml_nrows(t); r++) {
+                std::vector<int32_t> data(t->ne[0]);
+                for (int i = 0; i < t->ne[0]; i++) {
+                    data[i] = i % n_mats;
+                }
+                std::shuffle(data.begin(), data.end(), rng);
+                ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(int32_t));
+            }
+        } else {
+            init_tensor_uniform(t);
+        }
+    }
+}
+
 // GGML_OP_MUL_MAT_ID
 struct test_mul_mat_id : public test_case {
     const ggml_type type_a;
@@ -3494,10 +3593,9 @@ struct test_mul_mat_id : public test_case {
     const int64_t m;
     const int64_t n;
     const int64_t k;
-    const uint32_t o; // number of outputs
 
     std::string vars() override {
-        return VARS_TO_STR9(type_a, type_b, n_mats, n_used, b, m, n, k, o);
+        return VARS_TO_STR8(type_a, type_b, n_mats, n_used, b, m, n, k);
     }
 
     double max_nmse_err() override {
@@ -3511,9 +3609,69 @@ struct test_mul_mat_id : public test_case {
 
     test_mul_mat_id(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
             int n_mats = 8, int n_used = 2, bool b = false,
-            int64_t m = 32, int64_t n = 32, int64_t k = 32, uint32_t o = 1)
+            int64_t m = 32, int64_t n = 32, int64_t k = 32)
         : type_a(type_a), type_b(type_b), n_mats(n_mats), n_used(n_used), b(b),
-            m(m), n(n), k(k), o(o) {
+            m(m), n(n), k(k) {
+            GGML_ASSERT(n_used <= n_mats);
+        }
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        // C^T = A * B^T: (k, m) * (k, n) => (m, n)
+        ggml_tensor * as = ggml_new_tensor_3d(ctx, type_a, k, m, n_mats);
+        ggml_set_name(as, "as");
+
+        ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_mats, n);
+        ggml_set_name(ids, "ids");
+        if (n_used != n_mats) {
+            ids = ggml_view_2d(ctx, ids, n_used, n, ids->nb[1], 0);
+            ggml_set_name(ids, "view_of_ids");
+        }
+
+        ggml_tensor * b = ggml_new_tensor_3d(ctx, type_b, k, this->b ? 1 : n_used, n);
+        ggml_set_name(b, "b");
+
+        ggml_tensor * out = ggml_mul_mat_id(ctx, as, b, ids);
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        init_mul_mat_id_tensors(ctx, n_mats);
+    }
+};
+
+// GGML_OP_MUL_MAT_ID + GGML_OP_ADD or GGML_OP_MUL
+struct test_mul_mat_id_fusion : public test_case {
+    const ggml_type type_a;
+    const ggml_type type_b;
+    const int n_mats;
+    const int n_used;
+    const bool b; // broadcast b matrix
+    const int64_t m;
+    const int64_t n;
+    const int64_t k;
+    const uint32_t o; // number of outputs
+    const bool mul;
+
+    std::string vars() override {
+        return VARS_TO_STR10(type_a, type_b, n_mats, n_used, b, m, n, k, o, mul);
+    }
+
+    double max_nmse_err() override {
+        return 5e-4;
+    }
+
+    uint64_t op_flops(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return 2 * m * k * n * n_used;
+    }
+
+    test_mul_mat_id_fusion(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
+            int n_mats = 8, int n_used = 2, bool b = false,
+            int64_t m = 32, int64_t n = 32, int64_t k = 32, uint32_t o = 1, bool mul = false)
+        : type_a(type_a), type_b(type_b), n_mats(n_mats), n_used(n_used), b(b),
+            m(m), n(n), k(k), o(o), mul(mul) {
             GGML_ASSERT(n_used <= n_mats);
         }
 
@@ -3542,35 +3700,25 @@ struct test_mul_mat_id : public test_case {
             out = ggml_add(ctx, out, out2);
         }
 
+        if (mul) {
+            std::array<int64_t, 4> ne { 1, out->ne[1], out->ne[2], out->ne[3] };
+            ne[0] = 1;
+            ggml_tensor * m = ggml_new_tensor(ctx, out->type, 4, ne.data());
+            out = ggml_mul(ctx, out, m);
+        }
+
         return out;
     }
 
     void initialize_tensors(ggml_context * ctx) override {
-        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
-            if (t->type == GGML_TYPE_I32) {
-                if (ggml_is_view_op(t->op)) { continue; }
-                std::random_device rd;
-                std::default_random_engine rng(rd());
-                // ids
-                for (int64_t r = 0; r < ggml_nrows(t); r++) {
-                    std::vector<int32_t> data(t->ne[0]);
-                    for (int i = 0; i < t->ne[0]; i++) {
-                        data[i] = i % n_mats;
-                    }
-                    std::shuffle(data.begin(), data.end(), rng);
-                    ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(int32_t));
-                }
-            } else {
-                init_tensor_uniform(t);
-            }
-        }
+        init_mul_mat_id_tensors(ctx, n_mats);
     }
 
-    bool run_whole_graph() override { return o > 1; }
+    bool run_whole_graph() override { return true; }
 
     std::string op_desc(ggml_tensor * t) override {
         GGML_UNUSED(t);
-        return ggml_op_name(GGML_OP_MUL_MAT_ID);
+        return "MUL_MAT_ID_FUSION";
     }
 };
 
@@ -4809,60 +4957,6 @@ struct test_topk_moe: public test_case {
     }
 };
 
-struct test_moe_expert_reduce : public test_case {
-    const int64_t n_embd;
-    const int64_t n_tokens;
-    const int64_t n_expert_used;
-
-    test_moe_expert_reduce(int64_t n_embd = 64, int64_t n_tokens = 5, int64_t n_expert_used = 4)
-        : n_embd(n_embd), n_tokens(n_tokens), n_expert_used(n_expert_used) {
-        GGML_ASSERT(n_expert_used > 1);
-    }
-
-    std::string vars() override {
-        return VARS_TO_STR3(n_embd, n_tokens, n_expert_used);
-    }
-
-    std::string op_desc(ggml_tensor * t) override {
-        GGML_UNUSED(t);
-        return "MOE_EXPERT_REDUCE";
-    }
-
-    bool run_whole_graph() override { return true; }
-
-    ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * experts = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, n_expert_used, n_tokens);
-        ggml_set_name(experts, "experts");
-
-        ggml_tensor * weights = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 1, n_expert_used, n_tokens);
-        ggml_set_name(weights, "weights");
-
-        ggml_tensor * weighted = ggml_mul(ctx, experts, weights);
-        ggml_set_name(weighted, "weighted_experts");
-
-        std::vector<ggml_tensor *> expert_views(n_expert_used);
-        for (int64_t i = 0; i < n_expert_used; ++i) {
-            expert_views[i] = ggml_view_2d(ctx, weighted, n_embd, n_tokens, weighted->nb[2], i * weighted->nb[1]);
-
-            std::string name = "expert_view_" + std::to_string(i);
-            ggml_set_name(expert_views[i], name.c_str());
-            ggml_build_forward_expand(gf, expert_views[i]);
-        }
-
-        ggml_tensor * moe_out = expert_views[0];
-        for (int64_t i = 1; i < n_expert_used; ++i) {
-            moe_out = ggml_add(ctx, moe_out, expert_views[i]);
-
-            std::string name = "expert_add_" + std::to_string(i - 1);
-            ggml_set_name(moe_out, name.c_str());
-        }
-
-        ggml_set_name(moe_out, "moe_out");
-
-        return moe_out;
-    }
-};
-
 struct test_mul_mat_vec_fusion : public test_case {
     const ggml_type type;
     const ggml_glu_op glu_op;
@@ -4911,8 +5005,10 @@ struct test_mul_mat_vec_fusion : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         if (!use_id) {
-            std::array<int64_t, 4> ne = {k, m, 1, 1};
-            std::array<int64_t, 4> ne0 = {k, n, 1, 1};
+            const int              channels = 4;
+            const int              samples  = 2;
+            std::array<int64_t, 4> ne       = { k, m, channels, samples };
+            std::array<int64_t, 4> ne0      = { k, n, channels, samples };
 
             ggml_tensor * cur  = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne.data());
             ggml_tensor * gate = with_gate ? ggml_new_tensor(ctx, type, 4, ne0.data()) : nullptr;
@@ -4920,14 +5016,14 @@ struct test_mul_mat_vec_fusion : public test_case {
 
             ggml_tensor * ffn_up = ggml_mul_mat(ctx, up, cur);
             if (with_bias) {
-                std::array<int64_t, 4> bias_ne = {ffn_up->ne[0], 1, 1, 1};
+                std::array<int64_t, 4> bias_ne = { ffn_up->ne[0], 1, channels, samples };
                 ggml_tensor * up_bias = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, bias_ne.data());
                 ffn_up = ggml_add(ctx, ffn_up, up_bias);
             }
 
             ggml_tensor * ffn_gate = with_gate ? ggml_mul_mat(ctx, gate, cur) : nullptr;
             if (with_bias && with_gate) {
-                std::array<int64_t, 4> bias_ne = {ffn_gate->ne[0], 1, 1, 1};
+                std::array<int64_t, 4> bias_ne   = { ffn_gate->ne[0], 1, channels, samples };
                 ggml_tensor * gate_bias = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, bias_ne.data());
                 ffn_gate = ggml_add(ctx, ffn_gate, gate_bias);
             }
@@ -4971,24 +5067,7 @@ struct test_mul_mat_vec_fusion : public test_case {
                 init_tensor_uniform(t);
             }
         } else {
-            std::random_device rd;
-            std::default_random_engine rng(rd());
-            for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
-                if (t->type == GGML_TYPE_I32) {
-                    if (ggml_is_view_op(t->op)) { continue; }
-                    // ids
-                    for (int64_t r = 0; r < ggml_nrows(t); r++) {
-                        std::vector<int32_t> data(t->ne[0]);
-                        for (int i = 0; i < t->ne[0]; i++) {
-                            data[i] = i % n_mats;
-                        }
-                        std::shuffle(data.begin(), data.end(), rng);
-                        ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(int32_t));
-                    }
-                } else {
-                    init_tensor_uniform(t);
-                }
-            }
+            init_mul_mat_id_tensors(ctx, n_mats);
         }
     }
 
@@ -5144,7 +5223,9 @@ struct test_interpolate : public test_case {
     const uint32_t mode = GGML_SCALE_MODE_NEAREST;
 
     std::string vars() override {
-        return VARS_TO_STR4(type, ne, ne_tgt, mode);
+        ggml_scale_mode mode = (ggml_scale_mode)(this->mode & 0xFF);
+        std::string flags = (this->mode & GGML_SCALE_FLAG_ALIGN_CORNERS) ? "align_corners" : "none";
+        return VARS_TO_STR5(type, ne, ne_tgt, mode, flags);
     }
 
     test_interpolate(ggml_type type = GGML_TYPE_F32,
@@ -6648,6 +6729,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_cpy(GGML_TYPE_F16, GGML_TYPE_F16, {256, 4, 1, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true));
     test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {256, 4, 1, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true));
     test_cases.emplace_back(new test_cpy(GGML_TYPE_BF16, GGML_TYPE_BF16, {256, 4, 1, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true));
+    test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {256, 1, 4, 1}, {1, 2, 0, 3}, {0, 0, 0, 0}));
 
     test_cases.emplace_back(new test_cont());
     test_cases.emplace_back(new test_cont(GGML_TYPE_F32, {2, 1, 1 ,1}));
@@ -6750,6 +6832,22 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         }
     }
 
+    for (auto multi_add : {false, true}) {
+        for (auto set_rows : {false, true}) {
+            for (auto rope : {GGML_ROPE_TYPE_NORMAL, GGML_ROPE_TYPE_NEOX}) {
+                test_cases.emplace_back(new test_rms_norm_mul_rope({768, 1, 1, 1}, 1e-6f, multi_add, set_rows, rope));
+                test_cases.emplace_back(new test_rms_norm_mul_rope({768, 3, 1, 1}, 1e-6f, multi_add, set_rows, rope));
+                test_cases.emplace_back(new test_rms_norm_mul_rope({768, 3, 5, 1}, 1e-6f, multi_add, set_rows, rope));
+                test_cases.emplace_back(new test_rms_norm_mul_rope({128, 32, 2, 1}, 1e-6f, multi_add, set_rows, rope));
+                test_cases.emplace_back(new test_rms_norm_mul_rope({128, 4, 2, 1}, 1e-6f, multi_add, set_rows, rope));
+                test_cases.emplace_back(new test_rms_norm_mul_rope({128, 32, 50, 1}, 1e-6f, multi_add, set_rows, rope));
+                test_cases.emplace_back(new test_rms_norm_mul_rope({128, 4, 50, 1}, 1e-6f, multi_add, set_rows, rope));
+                test_cases.emplace_back(new test_rms_norm_mul_rope({8192, 2, 2, 1}, 1e-6f, multi_add, set_rows, rope));
+                test_cases.emplace_back(new test_rms_norm_mul_rope({8192, 2, 2, 1}, 1e-6f, multi_add, set_rows, rope));
+            }
+        }
+    }
+
     test_cases.emplace_back(new test_l2_norm(GGML_TYPE_F32, {64, 5, 4, 3}, 1e-12f));
 
     for (int64_t d_conv : {3, 4}) {
@@ -6896,6 +6994,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F32, GGML_TYPE_F32, 16, 32, 32, { 1,  1}, {1, 1}, {0, 1, 2, 3}, 64, 3));
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F32, GGML_TYPE_F32, 64, 77, 77, {12,1}, {1,1}));
 
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q4_0, GGML_TYPE_F32, 576, 512, 576, {1,1}, {1,1}));
+
 #if 0
     // test the mat-mat path for Metal
     for (int k = 1; k < 512; ++k) {
@@ -6941,7 +7041,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     }
 
     test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 1, 1, false, 8, 16, 1));
-    test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 16, 16, false, 32, 32, 32, 3));
+    test_cases.emplace_back(new test_mul_mat_id_fusion(GGML_TYPE_F16, GGML_TYPE_F32, 16, 16, false, 32, 32, 32, 3));
 
     // gpt-oss issue with Vulkan mmq_id
     test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_MXFP4, GGML_TYPE_F32, 32, 2, false, 2880, 32, 2880));
@@ -6978,6 +7078,15 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         }
     }
 
+    for (int bs : {1, 4, 512}) {
+        for (ggml_type type_a : {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_Q4_0, GGML_TYPE_Q4_K}) {
+            for (ggml_type type_b : {GGML_TYPE_F32}) {
+                // test with mul after (ffn_moe_weighted)
+                test_cases.emplace_back(new test_mul_mat_id_fusion(type_a, type_b, 128, 8, false, 768, bs, 2048, 1, true));
+            }
+        }
+    }
+
     for (ggml_type type_a : base_types) {
         for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) {
             for (int n : {1, 16}) {
@@ -7186,15 +7295,17 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {2, 8, 8192, 1}, order)); // bailingmoe2 (group selection)
     }
 
-    for (ggml_scale_mode mode : {GGML_SCALE_MODE_NEAREST, GGML_SCALE_MODE_BILINEAR}) {
+    for (ggml_scale_mode mode : {GGML_SCALE_MODE_NEAREST, GGML_SCALE_MODE_BILINEAR, GGML_SCALE_MODE_BICUBIC}) {
         test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, {512, 512, 3, 2}, 2, mode));
         test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, {512, 512, 3, 2}, 2, mode, true));
         test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {2, 5,  7, 11}, {5, 7, 11, 13}, mode));
         test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {5, 7, 11, 13}, {2, 5,  7, 11}, mode));
     }
-    test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {2, 5,  7, 11}, {5, 7, 11, 13}, GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ALIGN_CORNERS));
-    test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {1, 4, 3, 2}, {2, 8, 3, 2}, GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ALIGN_CORNERS));
-    test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {4, 1, 3, 2}, {1, 1, 3, 2}, GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ALIGN_CORNERS));
+    for (ggml_scale_mode mode : {GGML_SCALE_MODE_BILINEAR, GGML_SCALE_MODE_BICUBIC}) {
+        test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {2, 5, 7, 11}, {5, 7, 11, 13}, mode | GGML_SCALE_FLAG_ALIGN_CORNERS));
+        test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {1, 4, 3, 2}, {2, 8, 3, 2}, mode | GGML_SCALE_FLAG_ALIGN_CORNERS));
+        test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {4, 1, 3, 2}, {1, 1, 3, 2}, mode | GGML_SCALE_FLAG_ALIGN_CORNERS));
+    }
 
     test_cases.emplace_back(new test_sum());
     test_cases.emplace_back(new test_sum_rows());
@@ -7323,10 +7434,6 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_topk_moe({ 8, 22, 1, 1 }, 4, /*with_norm*/ false, /*delayed_softmax*/ true));
     test_cases.emplace_back(new test_topk_moe({ 32, 22, 1, 1 }, 8, /*with_norm*/ false, /*delayed_softmax*/ true));
 
-    test_cases.emplace_back(new test_moe_expert_reduce(1024, 5, 4));
-    test_cases.emplace_back(new test_moe_expert_reduce(80, 3, 6));
-    test_cases.emplace_back(new test_moe_expert_reduce(80, 3, 7));
-
 #if 0
     // these tests are disabled to save execution time, sbut they can be handy for debugging
     test_cases.emplace_back(new test_llama(2, true));
@@ -7438,7 +7545,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
     for (int bs : {1, 4, 8, 32, 64, 128, 256, 512}) {
         for (ggml_type type_a : {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0, GGML_TYPE_Q4_K, GGML_TYPE_Q6_K, GGML_TYPE_IQ2_XS}) {
             for (ggml_type type_b : {GGML_TYPE_F32}) {
-                test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, 128, 8, false, 768, bs, 2048, 1));
+                test_cases.emplace_back(new test_mul_mat_id_fusion(type_a, type_b, 128, 8, false, 768, bs, 2048, 1));
             }
         }
     }
@@ -7446,7 +7553,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
     for (int bs : {1, 4, 8, 32, 64, 128, 256, 512}) {
         for (ggml_type type_a : {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0, GGML_TYPE_Q4_K, GGML_TYPE_Q6_K, GGML_TYPE_IQ2_XS}) {
             for (ggml_type type_b : {GGML_TYPE_F32}) {
-                test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, 32, 4, false, 1792, bs, 2048, 1));
+                test_cases.emplace_back(new test_mul_mat_id_fusion(type_a, type_b, 32, 4, false, 1792, bs, 2048, 1));
             }
         }
     }
@@ -7456,7 +7563,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
     for (int bs : {1, 4, 8, 512}) {
         for (ggml_type type_a : {GGML_TYPE_MXFP4}) {
             for (ggml_type type_b : {GGML_TYPE_F32}) {
-                test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, 32, 4, false, 2880, bs, 2880, 1));
+                test_cases.emplace_back(new test_mul_mat_id_fusion(type_a, type_b, 32, 4, false, 2880, bs, 2880, 1));
             }
         }
     }

tools/batched-bench/batched-bench.cpp

@@ -23,7 +23,8 @@ int main(int argc, char ** argv) {
 
     common_init();
 
-    int is_pp_shared = params.is_pp_shared;
+    int is_pp_shared   = params.is_pp_shared;
+    int is_tg_separate = params.is_tg_separate;
 
     std::vector<int> n_pp = params.n_pp;
     std::vector<int> n_tg = params.n_tg;
@@ -72,8 +73,8 @@ int main(int argc, char ** argv) {
 
     // decode in batches of ctx_params.n_batch tokens
     auto decode_helper = [](llama_context * ctx, llama_batch & batch, int32_t n_batch, bool synchronize) {
-        for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
-            const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
+        for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
+            const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);
 
             llama_batch batch_view = {
                 n_tokens,
@@ -113,7 +114,7 @@ int main(int argc, char ** argv) {
 
     if (!params.batched_bench_output_jsonl) {
         LOG("\n");
-        LOG("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, params.n_batch, params.n_ubatch, int(params.flash_attn_type), params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
+        LOG("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, is_tg_separate = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, params.n_batch, params.n_ubatch, int(params.flash_attn_type), is_pp_shared, is_tg_separate, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
         LOG("\n");
         LOG("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s");
         LOG("|%6s-|-%6s-|-%4s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "----", "------", "--------", "--------", "--------", "--------", "--------", "--------");
@@ -172,16 +173,35 @@ int main(int argc, char ** argv) {
 
                 const auto t_tg_start = ggml_time_us();
 
-                for (int i = 0; i < tg; ++i) {
-                    common_batch_clear(batch);
-
+                if (is_tg_separate) {
+                    // decode pattern:
+                    // 0 0 0 ... 1 1 1 ... 2 2 2 ... 3 3 3 ...
                     for (int j = 0; j < pl; ++j) {
-                        common_batch_add(batch, get_token_rand(), pp + i, { j }, true);
-                    }
+                        for (int i = 0; i < tg; ++i) {
+                            common_batch_clear(batch);
 
-                    if (!decode_helper(ctx, batch, ctx_params.n_batch, true)) {
-                        LOG_ERR("%s: llama_decode() failed\n", __func__);
-                        return 1;
+                            common_batch_add(batch, get_token_rand(), pp + i, { j }, true);
+
+                            if (!decode_helper(ctx, batch, ctx_params.n_batch, true)) {
+                                LOG_ERR("%s: llama_decode() failed\n", __func__);
+                                return 1;
+                            }
+                        }
+                    }
+                } else {
+                    // decode pattern:
+                    // 0123 0123 0123 ...
+                    for (int i = 0; i < tg; ++i) {
+                        common_batch_clear(batch);
+
+                        for (int j = 0; j < pl; ++j) {
+                            common_batch_add(batch, get_token_rand(), pp + i, { j }, true);
+                        }
+
+                        if (!decode_helper(ctx, batch, ctx_params.n_batch, true)) {
+                            LOG_ERR("%s: llama_decode() failed\n", __func__);
+                            return 1;
+                        }
                     }
                 }
 

tools/llama-bench/llama-bench.cpp

@@ -1919,6 +1919,12 @@ struct sql_printer : public printer {
     }
 };
 
+struct ctx_state {
+    int depth = 0; // in tokens
+
+    std::vector<uint8_t> buf; // the llama_context state buffer
+};
+
 static bool test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_threads) {
     llama_set_n_threads(ctx, n_threads, n_threads);
 
@@ -2051,6 +2057,10 @@ int main(int argc, char ** argv) {
     llama_model *               lmodel    = nullptr;
     const cmd_params_instance * prev_inst = nullptr;
 
+    // store the llama_context state at the previous depth that we performed a test
+    // ref: https://github.com/ggml-org/llama.cpp/pull/16944#issuecomment-3478151721
+    ctx_state cstate;
+
     int  params_idx   = 0;
     auto params_count = params_instances.size();
     for (const auto & inst : params_instances) {
@@ -2134,14 +2144,37 @@ int main(int argc, char ** argv) {
             llama_memory_clear(llama_get_memory(ctx), false);
 
             if (t.n_depth > 0) {
-                if (params.progress) {
-                    fprintf(stderr, "llama-bench: benchmark %d/%zu: depth run %d/%d\n", params_idx, params_count,
-                            i + 1, params.reps);
+                bool is_cached = t.n_depth == cstate.depth;
+
+                if (is_cached) {
+                    // if previously we have computed at this depth, just restore the state
+                    const size_t ret = llama_state_seq_set_data(ctx, cstate.buf.data(), cstate.buf.size(), 0);
+                    if (ret == 0) {
+                        // if the old state is incompatible with the current context - reprocess from scratch
+                        is_cached = false;
+                    }
                 }
-                bool res = test_prompt(ctx, t.n_depth, t.n_batch, t.n_threads);
-                if (!res) {
-                    fprintf(stderr, "%s: error: failed to run depth\n", __func__);
-                    exit(1);
+
+                if (!is_cached) {
+                    if (params.progress) {
+                        fprintf(stderr, "llama-bench: benchmark %d/%zu: depth run %d/%d\n", params_idx, params_count,
+                                i + 1, params.reps);
+                    }
+                    bool res = test_prompt(ctx, t.n_depth, t.n_batch, t.n_threads);
+                    if (!res) {
+                        fprintf(stderr, "%s: error: failed to run depth\n", __func__);
+                        exit(1);
+                    }
+
+                    // store the context state for reuse in later runs
+                    cstate.depth = t.n_depth;
+                    cstate.buf.resize(llama_state_seq_get_size(ctx, 0));
+                    llama_state_seq_get_data(ctx, cstate.buf.data(), cstate.buf.size(), 0);
+                } else {
+                    if (params.progress) {
+                        fprintf(stderr, "llama-bench: benchmark %d/%zu: depth run %d/%d (cached)\n", params_idx, params_count,
+                                i + 1, params.reps);
+                    }
                 }
             }
 

tools/mtmd/clip.cpp

@@ -160,13 +160,13 @@ enum patch_merge_type {
 };
 
 struct clip_hparams {
-    int32_t image_size;
-    int32_t patch_size;
-    int32_t n_embd;
-    int32_t n_ff;
-    int32_t projection_dim;
-    int32_t n_head;
-    int32_t n_layer;
+    int32_t image_size = 0;
+    int32_t patch_size = 0;
+    int32_t n_embd = 0;
+    int32_t n_ff = 0;
+    int32_t projection_dim = 0;
+    int32_t n_head = 0;
+    int32_t n_layer = 0;
     // idefics3
     int32_t image_longest_edge = 0;
     int32_t image_min_pixels = -1;
@@ -2683,6 +2683,9 @@ struct clip_model_loader {
                 }
             } else if (is_audio) {
                 get_u32(KEY_A_NUM_MEL_BINS, hparams.n_mel_bins);
+                // some hparams are unused, but still need to set to avoid issues
+                hparams.image_size = 0;
+                hparams.patch_size = 1;
 
             } else {
                 GGML_ASSERT(false && "unknown modality");

tools/mtmd/mtmd-helper.cpp

@@ -182,7 +182,7 @@ int32_t mtmd_helper_decode_image_chunk(
     }
 
     const llama_model * model = llama_get_model(lctx);
-    int n_mmproj_embd = llama_model_n_embd(model);
+    int n_mmproj_embd = llama_model_n_embd_inp(model);
     int n_pos_per_embd = mtmd_decode_use_mrope(ctx) ? 4 : 1;
 
     int32_t n_tokens = mtmd_input_chunk_get_n_tokens(chunk);

tools/mtmd/mtmd.cpp

@@ -163,7 +163,7 @@ struct mtmd_context {
         print_timings(ctx_params.print_timings),
         n_threads    (ctx_params.n_threads),
         media_marker (ctx_params.media_marker),
-        n_embd_text  (llama_model_n_embd(text_model))
+        n_embd_text  (llama_model_n_embd_inp(text_model))
     {
         if (std::string(ctx_params.image_marker) != MTMD_DEFAULT_IMAGE_MARKER) {
             throw std::runtime_error("custom image_marker is not supported anymore, use media_marker instead");

tools/server/README.md

@@ -512,7 +512,7 @@ These words will not be included in the completion, so make sure to add them to
 
 `timings_per_token`: Include prompt processing and text generation speed information in each response.  Default: `false`
 
-`return_progress`: Include prompt processing progress in `stream` mode. The progress will be contained inside `prompt_progress` with 3 values: `total`, `cache` and `processed`. The overall progress is `processed/total`, while the actual timed progress is `(processed-cache)/(total-cache)`. Default: `false`
+`return_progress`: Include prompt processing progress in `stream` mode. The progress will be contained inside `prompt_progress` with 4 values: `total`, `cache`, `processed`, and `time_ms`. The overall progress is `processed/total`, while the actual timed progress is `(processed-cache)/(total-cache)`. The `time_ms` field contains the elapsed time in milliseconds since prompt processing started. Default: `false`
 
 `post_sampling_probs`: Returns the probabilities of top `n_probs` tokens after applying sampling chain.
 

tools/server/server.cpp

@@ -1690,6 +1690,9 @@ struct server_slot {
         bool res = prompt_cache.load(prompt, tokens, ctx, id);
         if (!res) {
             SLT_WRN(*this, "%s", "failed to load prompt from cache\n");
+
+            llama_memory_seq_rm(llama_get_memory(ctx), id, -1, -1);
+            prompt.tokens.clear();
         }
     }
 
@@ -2823,6 +2826,8 @@ struct server_context {
                 send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
                 return false;
             }
+
+            SLT_INF(slot, "sampler chain: %s\n", common_sampler_print(slot.smpl).c_str());
         }
 
         // initialize draft batch
@@ -3076,7 +3081,7 @@ struct server_context {
             res->progress.total     = slot.task->n_tokens();
             res->progress.cache     = slot.n_prompt_tokens_cache;
             res->progress.processed = slot.prompt.tokens.size();
-            res->progress.time_ms   = (ggml_time_us() - slot.t_start_process_prompt / 1000);
+            res->progress.time_ms   = (ggml_time_us() - slot.t_start_process_prompt) / 1000;
         } else {
             res->content = tkn.text_to_send;
             res->tokens  = { tkn.tok };

tools/server/tests/unit/test_completion.py

@@ -1,6 +1,8 @@
 import pytest
 import requests
 import time
+import random
+
 from openai import OpenAI
 from utils import *
 
@@ -564,3 +566,43 @@ def test_cancel_request():
     time.sleep(1) # wait for HTTP_POLLING_SECONDS
     res = server.make_request("GET", "/slots")
     assert res.body[0]["is_processing"] == False
+
+
+# this test exercises the host-memory prompt cache
+# ref: https://github.com/ggml-org/llama.cpp/pull/16391
+# ref: https://github.com/ggml-org/llama.cpp/pull/17078
+def test_completion_prompt_cache():
+    global server
+    server.n_slots = 2
+    server.kv_unified = True
+    server.start()
+
+    for _ in range(16):
+        # generate alternating random prompts with variable lengths in order to get them in and out of the cache
+        r = random.randint(0, 4)
+        prompt = (" Hello " +  str(r)) * (40 + r)
+        n_prompt = (40 + r)*5 + 2
+        n_predict = random.randint(1, 8)
+
+        res = server.make_request(
+            "POST",
+            "/completion",
+            data={
+                "prompt": prompt,
+                "n_predict": n_predict,
+            },
+        )
+
+        assert res.status_code == 200
+        assert "content" in res.body
+        content = res.body["content"]
+        assert isinstance(content, str)
+        assert len(content) > 0
+
+        assert type(res.body["has_new_line"]) == bool
+        assert "timings" in res.body
+        timings = res.body["timings"]
+
+        assert "prompt_n" in timings and timings["prompt_n"] + timings["cache_n"] == n_prompt
+        assert "predicted_n" in timings and timings["predicted_n"] == n_predict
+        assert "tokens" in res.body and isinstance(res.body["tokens"], list)

tools/server/tests/unit/test_speculative.py

@@ -77,10 +77,10 @@ def test_different_draft_min_draft_max():
 
 def test_slot_ctx_not_exceeded():
     global server
-    server.n_ctx = 64
+    server.n_ctx = 256
     server.start()
     res = server.make_request("POST", "/completion", data={
-        "prompt": "Hello " * 56,
+        "prompt": "Hello " * 248,
         "temperature": 0.0,
         "top_k": 1,
         "speculative.p_min": 0.0,
@@ -91,19 +91,19 @@ def test_slot_ctx_not_exceeded():
 
 def test_with_ctx_shift():
     global server
-    server.n_ctx = 64
+    server.n_ctx = 256
     server.enable_ctx_shift = True
     server.start()
     res = server.make_request("POST", "/completion", data={
-        "prompt": "Hello " * 56,
+        "prompt": "Hello " * 248,
         "temperature": 0.0,
         "top_k": 1,
-        "n_predict": 64,
+        "n_predict": 256,
         "speculative.p_min": 0.0,
     })
     assert res.status_code == 200
     assert len(res.body["content"]) > 0
-    assert res.body["tokens_predicted"] == 64
+    assert res.body["tokens_predicted"] == 256
     assert res.body["truncated"] == True
 
 

tools/server/webui/src/routes/+layout.svelte

@@ -44,12 +44,12 @@
 			}
 		}
 
-		if (isCtrlOrCmd && event.shiftKey && event.key === 'o') {
+		if (isCtrlOrCmd && event.shiftKey && event.key === 'O') {
 			event.preventDefault();
 			goto('?new_chat=true#/');
 		}
 
-		if (event.shiftKey && isCtrlOrCmd && event.key === 'e') {
+		if (event.shiftKey && isCtrlOrCmd && event.key === 'E') {
 			event.preventDefault();
 
 			if (chatSidebar?.editActiveConversation) {